No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A snake model for object tracking in natural sequences
Abstract
Tracking moving objects in video sequences is a task that emerges in various fields of study: video analysis, computer vision, biomedical systems, etc. In the last decade, special attention has been drawn to problems concerning tracking in real-world environments, where moving objects do not obey any afore-known constraints about their nature and motion or the scenes they are moving in. Apart from the existence of noise and environmental changes, many problems are also concerned, due to background texture, complicated object motion, and deformable and/or articulated objects, changing their shape while moving along time. Another phenomenon in natural sequences is the appearance of occlusions between different objects, whose handling requires motion information and, in some cases, additional constraints. In this work, we revisit one of the most known active contours, the Snakes, and we propose a motion-based utilization of it, aiming at successful handling of the previously mentioned problems. The use of the object motion history and first order statistical measurements of it, provide us with information for the extraction of uncertainty regions, a kind of shape prior knowledge w.r.t. the allowed object deformations. This constraining also makes the proposed method efficient, handling the trade-off between accuracy and computation complexity. The energy minimization is approximated by a force-based approach inside the extracted uncertainty regions, and the weights of the total snake energy function are automatically estimated as respective weights in the resulting evolution force. Finally, in order to handle background complexity and partial occlusion cases, we introduce two rules, according to which the moving object region is correctly separated from the background, whereas the occluded boundaries are estimated according to the object's expected shape. To verify the performance of the proposed method, some experimental results are included, concerning different cases of object tracking, indoors and outdoors, with rigid and deformable objects, noisy and textured backgrounds, as well as appearance of occlusions.
... Contours are well-known descriptors of position and apparent shape of an object in the image. Contour tracking of a target in natural video sequences has been subject of many researches, where " Active Contour Model " (snake) [5] is proven to be a powerful tool, especially in combination with other techniques67891011. Although active contour based methods can usually handle target shape deformations, in most of contour tracking algorithms the target aspect change is assumed to be small or negligible. ...
... Peterfreund [6] introduced a Kalman-filter based active contour for object tracking. Techpenakis [7] proposed a method to extract an uncertainty region for the snake activity in every image frame, using optical flow and contour velocity information. However, these algorithms are not proper for tracking targets with large aspect change. ...
... Therefore, energy minimizing algorithms which try to find the global minimum and decrease the effect of initialization [13,14] are not usually suitable for tracking targets in cluttered backgrounds. In most of the methods that use snake for tracking, the snake initialization in each frame is a prediction of the desired solution, and the snake activity is limited so that it won't go far away from initialization, for example by using a prediction energy [10] or a restricted search band [7]. In our method we use the target motion model to predict the target contour in each frame and use it as the snake initialization. ...
In this paper we present a novel method for tracking contour of a moving rigid object in natural video sequences from a mobile camera. While a mobile camera is keeping track of a moving target, any change in relative orientation or position of the target and camera may cause a change in the view of target captured by the camera. This is called the target aspect change and known as a challenge in tracking. Especially, a large aspect change may reveal some previously hidden parts of the target, and this is the case we have handled in our algorithm. In proposed contour tracking method the motion model of the target is estimated using corner matching and LMedS statistical method. This model is used together with an active contour (snake) to track the target efficiently. The snake we propose is capable of swelling locally, so that it can swell and include newly appeared parts of the target in case of a large aspect change. Detecting target newly appeared parts, is based on the fact that a part of target should move consistently with other parts and inconsistently with the background. When a new part is detected the nearby part of the snake is stimulated to swell. In this way the snake will include any new part of the target revealed by a large aspect change. Several experiments have been conducted to show the promise of proposed algorithm.
... The use of the object motion history and first order statistical measurements of object motion, provided the information for the extraction of uncertainty regions [24]. To handle background complexity and partial occlusion cases, [24] introduced two rules, according to which the moving object region is correctly separated from the background, whereas the occluded boundaries are estimated according to the object's expected shape. ...
... The use of the object motion history and first order statistical measurements of object motion, provided the information for the extraction of uncertainty regions [24]. To handle background complexity and partial occlusion cases, [24] introduced two rules, according to which the moving object region is correctly separated from the background, whereas the occluded boundaries are estimated according to the object's expected shape. ...
... It is used as a high flexible technique. Active contour alone is not able to track mobile targets with high precision in video images and usually used in combination with other techniques1234. In [1] Kalman filter is introduced in order to better performance of active contour. [2] Used from optical flow data and contour motion speed in specifying a region for active contour in each frame. ...
... for tracking. One of most popular method of tracking is active contour. It is used as a high flexible technique. Active contour alone is not able to track mobile targets with high precision in video images and usually used in combination with other techniques1234. In [1] Kalman filter is introduced in order to better performance of active contour. [2] Used from optical flow data and contour motion speed in specifying a region for active contour in each frame. [3] Used also active contour to correct regions that obtained from color and motion partitioning in each single image. In [4] affine transform is used to estimate motion model in two sequential single images to improve the perfo ...
In this paper, a new method for contour tracking of mobile target in video sequences is presented. Proposed method helps to track variety of targets exactly while the camera is moving. In this study, a new type of active contour is used with a way for estimating motion model of the target. The novelty of this paper comes from using a new color to gray level transform instead of conventional counterpart. Estimating motion model of the target uses color and location information together. This information helps the proposed method to be robust against the aspect change. Our method is compared with two other methods: tracking via single active contour and estimating motion model by using affine transform. Experimental results illustrate our method to outperform the prior ones.
... Consistent distinction of the player and the incoming poster from the left can be achieved without being affected by camera operations as observed throughout the sequence. Several proposed tracking techniques use motion information as an automatic initial guess for object's position or for improving an incremental tracking approach [11, 25, 28]. Generally speaking, motion estimation methods are computationally intensive and prone to noise. ...
... Although the moving objects in a sequence are usually important and have to be tracked there are cases that static objects (e.g. a scene with a camera pan showing a moving object and a photo/painting on the wall) play also a considerable role. In an attempt to emphasize the power of VA as a preprocessing step we provide a short discussion on the spatiotemporal VA's results of the " table tennis " sequence and the advantages it offers against a robust motion estimation technique that is used as an initial step in a tracking approach we proposed in [11]. The magnitude of the motion field generated by Black & Anandan's method [26] is shown 55 115 122Figure 6 Results on the " table-tennis " sequence (numbers correspond to frames). ...
Research Paper The human visual system (HVS) has the ability to fixate quickly on the most informative (salient) regions of a scene and therefore reducing the inherent visual uncertainty. Computational visual attention (VA) schemes have been proposed to account for this important characteristic of the HVS. A video analysis framework based on a spatiotemporal VA model is presented. A novel scheme has been proposed for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. To achieve this goal, a common, image-oriented computational model of saliency-based visual attention is extended to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step to obtain a compact representation of the visual content in the form of salient events/objects. The model has been implemented, and qualitative as well as quantitative examples illustrating its performance are shown.
... Consistent distinction of the player and the incoming poster from the left can be achieved without being affected by camera operations as observed throughout the sequence. Several proposed tracking techniques use motion information as an automatic initial guess for an object's position or for improving an incremental tracking approach [13, 14]. Generally speaking, motion estimation methods are computationally intensive and prone to noise. ...
... Although the moving objects in a sequence are usually important and have to be tracked there are cases that static objects (e.g. a scene with a camera pan showing a moving object and a photo/painting on the wall) play also a considerable role. In an attempt to emphasize the power of VA as a preprocessing step we provide a short discussion on the spatiotemporal VA's results of the " table tennis " sequence and the advantages it offers against a robust motion estimation technique that is used as an initial step in a tracking approach we proposed in [13]. The magnitude of the motion field (square root of motion vectors in x-and ydirections ) generated by Black & Anandan's method [15] is shown in the third column ofFig. 5. Notice how the zooming effect (frames 55, 75) affects the motion field and how hard it is to automatically distinguish the objects even with a refined motion segmentation technique. ...
In natural vision, we center our fixation on the most informative points in a scene in order to reduce our overall uncertainty about the scene and help interpret it. Even if we are looking for a specific stimulus around us, we face a great amount of uncertainty since that stimulus could be in any spatial location. Visual attention (VA) schemes have been proposed by researchers to account for the ability of the human eye to quickly fixate on informative regions. Recently, VA in images, and especially saliency-based VA, became an active research topic of the computer vision community. The proposed work provides an extension towards VA in video sequences by integrating spatiotemporal information. The potential applications include video classification, scene understanding, surveillance and segmentation
... Examples of probabilistic reasoning and state estimation techniques are plentiful in the literature. Those that incorporate geometry information for visual tracking[40,41,42,43,44,45,46] generally rely on having rich and accurate information delivered ...
This paper describes an interdisciplinary approach to geometry modeling of geospatial boundaries. The objective is to extract surfaces from irregular spatial patterns using differential geometry and obtain coherent directional predictions along the boundary of extracted surfaces to enable more targeted sampling and exploration. Specific difficulties of the data include sparsity, incompleteness, causality and resolution disparity. Surface slopes are estimated using only sparse samples from cross-sections within a geological domain with no other information at intermediate depths. From boundary detection to subsurface reconstruction, processes are automated in between. The key problems to be solved are boundary extraction, region correspondence and propagation of the boundaries via contour morphing. Established techniques from computational physics, computer vision and signal processing are used with appropriate modifications to address challenges in each area. To facilitate boundary extraction, an edge map synthesis procedure is presented. This works with connected component analysis, anisotropic diffusion and active contours to convert unordered points into regularized boundaries. For region correspondence, component relationships are handled via graphical decomposition. FFT-based spatial alignment strategies are used in region merging and splitting scenarios. Shape changes between aligned regions are described by contour metamorphosis. Specifically, local spatial deformation is modeled by PDE and computed using level-set methods. Directional predictions are obtained using particle trajectories by following the evolving boundary. However, when a branching point is encountered, particles may lose track of the wavefront. To overcome this, a curvelet backtracking algorithm has been proposed to recover information for boundary segments without particle coverage to minimize shape distortion.
... Examples of probabilistic reasoning and state estimation techniques are plentiful in the literature. Those that incorporate geometry information for visual tracking[40,41,42,43,44,45,46] generally rely on having rich and accurate information delivered ...
This paper describes an interdisciplinary approach to geometry modeling of geospatial boundaries. The objective is to extract surfaces from irregular spatial patterns using differential geometry and obtain coherent directional predictions along the boundary of extracted surfaces to enable more targeted sampling and exploration. Specific difficulties of the data include sparsity, incompleteness, causality and resolution disparity. Surface slopes are estimated using only sparse samples from cross-sections within a geological domain with no other information at intermediate depths. From boundary detection to subsurface reconstruction, processes are automated in between. The key problems to be solved are boundary extraction, region correspondence and propagation of the boundaries via contour morphing. Established techniques from computational physics, computer vision and signal processing are used with appropriate modifications to address challenges in each area. To facilitate boundary extraction, an edge map synthesis procedure is presented. This works with connected component analysis, anisotropic diffusion and active contours to convert unordered points into regularized boundaries. For region correspondence, component relationships are handled via graphical decomposition. FFT-based spatial alignment strategies are used in region merging and splitting scenarios. Shape changes between aligned regions are described by contour metamorphosis. Specifically, local spatial deformation is modeled by PDE and computed using level-set methods. Directional predictions are obtained using particle trajectories by following the evolving boundary. However, when a branching point is encountered, particles may lose track of the wavefront. To overcome this, a curvelet backtracking algorithm has been proposed to recover information for boundary segments without particle coverage to minimize shape distortion.
... In recent years, computer vision systems have made big advances in both the theory and algorithms of image processing as well as in practical applications. For example, a 3D stereo object in space may be captured as a 2D plane image by a charge-coupled device camera through image processing methods-such as image transforms, image enhancement, thresholding, filtering, edge detection, and thinning [1,2]. That object may then be used as image information for applications of feature extraction, recognition, visual servicing, and so forth [2][3][4] there are still many core technologies to be achieved before computer vision can be compared to human vision [5,6]. ...
Augmented reality technology is applied so that driving tests may be performed in various environments using a virtual reality scenario with the ultimate goal of improving visual and interactive effects of simulated drivers. Environmental conditions simulating a real scenario are created using an augmented reality structure, which guarantees the test taker’s security since they are not subject to real-life elements and dangers. Furthermore, the accuracy of tests conducted through virtual reality is not influenced by either environmental or human factors. Driver posture is captured in real time using Kinect’s depth perception function and then applied to driving simulation effects that are emulated by Unity3D’s gaming technology. Subsequently, different driving models may be collected through different drivers. In this research, nearly true and realistic street environments are simulated to evaluate driver behavior. A variety of different visual effects are easily available to effectively reduce error rates, thereby significantly improving test security as well as the reliability and reality of this project. Different situation designs are simulated and evaluated to increase development efficiency and build more security verification test platforms using such technology in conjunction with driving tests, vehicle fittings, environmental factors, and so forth.
... In order for people to perform test with VR scenarios in various different environments and experience real feeling, in addition to creating environmental conditions simulating real scenarios, we will employ Unity, software, to make a VR system and use in conjunction with various scenarios. Subsequently, it may be promoted to smart phone platforms for use [10,11]. With VR for simulation, not only it is safe and more convenient, but also the test accuracy will not be influenced by the environmental factors introduced by real test [12,13]. ...
A virtual reality (VR) driver tracking verification system is created, of which the application to stereo image tracking and positioning accuracy is researched in depth. In the research, the feature that the stereo vision system has image depth is utilized to improve the error rate of image tracking and image measurement. In a VR scenario, the function collecting behavioral data of driver was tested. By means of VR, racing operation is simulated and environmental (special weathers such as raining and snowing) and artificial (such as sudden crossing road by pedestrians, appearing of vehicles from dead angles, roadblock) variables are added as the base for system implementation. In addition, the implementation is performed with human factors engineered according to sudden conditions that may happen easily in driving. From experimental results, it proves that the stereo vision system created by the research has an image depth recognition error rate within 0.011%. The image tracking error rate may be smaller than 2.5%. In the research, the image recognition function of stereo vision is utilized to accomplish the data collection of driver tracking detection. In addition, the environmental conditions of different simulated real scenarios may also be created through VR.
... A control of the final contour must be thus checked. Many approaches have been proposed to erase these problems in [Jain 1998], and in [Tsechpenakis 2004]. Moreover the method fails sometimes for very complex shapes. ...
In this paper, we present the development of an advanced open source multi-lingual cooperative portal system (ASPICO) dedicated to semantic management, and to cooperative exchange for research and education purpose on digital cultural projects. Advantages of using ASPICO include greater flexibility for digital resource management, generic and systematic ontology-based metadata management, and better semantic access and delivery based on an innovative Information Modeling for Adaptive Management (IMAM).
... Model-based tracking [1,10] can be very efficient for a particular application but often cannot be easily extended to a different class of target, because they incorporate a priori information about the tracked objects. When objects are represented with parametric structures (usually curves), tracking is performed by seeking the lowest potential of a cost function of the parametric representation of the objects (shape, length, curvature, etc.): deformable models, snakes and appearance models [22,19,3] are the most popular techniques for tracking with small deformations. Another source of information used by tracking methods is the dynamic behavior of the object of interest. ...
In this paper, we propose a method to track multiple de-formable objects in sequences (with a static camera) in and beyond the visible spectrum by combining Gabor filtering and clustering. In a first step, a set of Gabor filter banks is used to filter the difference image between two consecu-tive frames. Then, the moving areas are sampled by ran-domly positioning particles in high magnitude area of the filtered image. Finally, these points are clustered to obtain one class for each moving object. The novelty in our method is in using cluster information from the previous frame to classify new particles in the current frame. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other.
... with total occlusions, when the object disappears from the scene for a number of frames, since no initial state is available when the object reappears. The case of large inter-frame displacements is also problematic. The object can be very distant from its previous position, so that the initial state might not be able converge to the new position. Tsechpenakis et al. (2004) solve these problems by initializing each frame, not using the previous segmentation, but employing the motion information available for that frame. In that work, motion features are only employed for initialization and the image potential depends only on spatial information. ...
In this chapter, a new active model for the segmentation of motion patterns from video sequences has been presented. It employs a motion representation based on composite energy features. It consists on the clustering of elementary band-pass features, which can be considered velocity tuned features. Integration is accomplished by extending the notion of phase congruence to spatio-temporal signals. The active model uses this motion information both for image potential definition and initialization of the model in each frame of the sequence.
... Active contours introduced by Kass with a model called Snake [16] had drawn attention due to their performance in various problems. Segmentation and shape modeling in single images proved effective by integrating region-based information, stochastic approaches and appropriate shape constrains [17,18]. Active contours merge image data and shape modeling through the definition of an linear energy function consisting of two terms: a data driven component (external energy), which depends on the image data, and a smoothness-driven component (internal energy) which enforces smoothness along the contour. ...
In this paper we present an approach to perform automated analysis of nematodes in population images. Occlusion, shape variability and structural noise make reliable recognition of individuals a task difficult. Our approach relies on shape and geometrical statistical data obtained from samples of segmented lines. We study how shape similarity in the objects of interest, is encoded in active contour energy component values and exploit them to define shape features. Without having to build a specific model or making explicit assumptions on the interaction of overlapping objects, our results show that a considerable number of individual can be extracted even in highly cluttered regions when shape information is consistent with the patterns found in a given sample set. Espol
... Model-based tracking [3] incorporate a priori information about the tracked objects. When objects are represented with parametric structures, tracking is performed by seeking the lowest potential of a cost function of the parametric representation of them: deformable models, snakes and appearance models [4, 5, 6] are the most popular techniques for tracking with small deformations. Another class of tracking methods is based on the dynamic behavior of the object of interest. ...
In this paper, we propose a method to track multiple deformable objects in video sequences using a recursive clustering scheme. In a first step, a set of Gabor filter banks is used to filter the difference image between two consecutive frames. Then, the moving areas are sampled by randomly positioning particles in high magnitude area of the filtered image. Finally, these points are clustered to obtain one class for each moving object. The novelty in our method is in using cluster information for the previous frame to classify new particles in the current frame. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other
... Active contours introduced by Kass with a model called snake [16] has drawn attention due to their performance in various problems. Segmentation and shape modeling in single images proved effective by integrating region-based information, stochastic approaches and appropriate shape constrains [17,18]. ...
In this paper we study how shape information encoded in contour energy components values can be used for detection of microscopic organisms in population images. We proposed features based on shape and geometrical statistical data obtained from samples of optimized contour lines integrated in the framework of Bayesian inference for recognition of individual specimens. Compared with common geometric features the results show that patterns present in the image allow better detection of a considerable amount of individuals even in cluttered regions when sufficient shape information is retained. Therefore providing an alternative to building a specific shape model or imposing specific constrains on the interaction of overlapping objects. Department of telecommunication and information processing, Ghent University, St-Pieters Nieuwstraat 41, B-9000, Ghent, Belgium Centro de Vision y Robotica, Facultad de Ingenieria en Electricidad y Computación, ESPOL University, Km 30.5 via perimetral, 09015863, Guayaquil, Ecuador
... Model-based tracking (Black and Jepson, 1998; Isard and MacCormick, 2001) can be very efficient for a particular application but often cannot be easily extended to a different class of target, because they incorporate prior information about the tracked objects. When objects are represented with parametric structures (usually curves), tracking is performed by seeking the lowest potential of a cost function of the parametric representation of the objects (shape, length, curvature , etc.): deformable models, snakes and appearance models (Tsechpenakis et al., 2004; Sclaroff and Isidoro, 2003; Chan et al., 1999) are the most popular techniques for tracking with small deformations. Another source of information used by tracking methods is the dynamic behavior of the object of interest. ...
In this paper, we propose a method to track multiple deformable objects in sequences (with a static camera) in and beyond the visible spectrum by combining Gabor filtering and clustering. The idea is to sample moving areas between two frames by randomly positioning samples over high magnitude area of a motion likelihood function. These points are then clustered to obtain one class for each moving object. The novelty in our method is in using cluster information from the previous frame to classify new samples in the current frame: we call that a recursive clustering. This makes our method robust to occlusions, objects entering and leaving the field of view, objects stopping and starting, and moving objects getting really close to each other.
... The reason for choosing an improved active contour model for comparison is twofold. First, in spite of the fact that active contour models, also known as snakes, have been proposed over a decade ago and initially not specifically proposed for the tracking of deformable objects, they are considered to be one of the standards for tracking deformable objects, as revealed by the literature on the topic over the years up to now [20], [37]- [41]. The second reason is that active contour models are the closest in concept to the research proposed in this paper. ...
Moving object tracking is one of the applied fields in artificial intelligence and robotic. The main objective of object tracking is to detect and locate targets in video frames of real scenes. Although various methods have been proposed for object tracking so far, tracking in challenging conditions remains an open issue. Recently, different evolutionary and heuristics algorithms like swarm intelligence have been used to address the tracking challenges, which have shown promising performance. In this paper, a new approach based on modified biogeography based optimization (mBBO) method is introduced. The BBO algorithm includes migration and mutation steps. In the migration phase, the search space is properly explored by sharing information between habitats and weaker solutions to improve their position. On the other hand, the mutation phase leads to diversity and change in solutions. In this algorithm, the elitist method has been also used to keep better solutions. The performance of modified BBO tracker has been evaluated on benchmark video datasets and compared with several other tracking methods. Experimental results demonstrate that the proposed method estimates the location of targets with high accuracy and achieves better performance and robustness compared to other trackers.
PSO/Snake hybrid algorithm is a merge of particle swarm optimization (PSO), a successful population based optimization technique, and the Snake model, a specialized image processing algorithm. In the PSO/Snake hybrid algorithm each particle in the population represents only a portion of the solution and the population, as a whole, will converge to the final complete solution. In this model there is a one-to-one relation between Snake model snaxels and PSO particles with the PSO’s kinematics being modified accordingly to the snake model dynamics. This paper provides an evaluative study on the performance of the customized PSO/Snake algorithm in solving a real-world problem from astrophysics domain and comparing the results with Gradient Path Labeling (GPL) image segmentation algorithm. The GPL algorithm segments the image into regions according to its intensity from where the relevant ones can be selected based on their features. A specific type of solar features called coronal bright points have been tracked in a series of solar images using both algorithms and the solar differential rotation is calculated accordingly. The final results are compared with those already reported in the literature.
Thread control method can promote system performance in the main purpose of this paper control system, divide by Thread CPU raise with time stage laying computer handle efficiency raise. Thread applies to the control system of the computer, the main purpose of this paper adopts the control of Thread in its procedure of image tracking and servo control system, the characteristic of utilizing Thread to carry out in step improves image Tracking and servo error rate controlled. In the main purpose of this paper compares with single thread to Thread, Thread improves Tracking pace and stabilizes time, especially vision Tracking and servo control will have a large number of materials, take to improve the system to carry out the pace so as to law this, to reach the cost and study the purpose.
Carbon fiber reinforced polymers (CFRP) and glass fiber reinforced polymers (GFRP) are used in the retrofit of aging infrastructures. The bonding condition between the FRP wrapping and the concrete structure plays an important role in the strengthening or upgrading of the original infrastructure. This study applies the infrared thermography to detect the air void, delamination and disbond inside the FRP layers or defect at the interface of the FRP and the concrete specimen. Flexible heating pad and strobe light are used as the thermal stimulation system. The IR camera and an image analysis system are adopted as the active pulse thermographic system. A level set method was applied to locate the defect area and contour of the image from the IR thermography. Experimental and numerical results show that this level set evolution without re-initialization method can find the defect area with higher efficiency and accuracy.
Particle swarm optimization (PSO) algorithm is a successful general problem solver, thanks to its computationally inexpensive mechanisms. On the other hand, snake model is a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. In this paper we discuss the suitability of a hybrid PSO/Snake algorithm for determining the differential rotation of the Sun's coronal bright points. In the Snake/PSO hybrid algorithm each particle in the population represents only a portion of the solution and the whole population altogether will converge to the final complete solution. In this model a one-to-one relation between Snake model snaxels and PSO particles have been created and PSO's evolution equations have been modified with snake model concepts. This hybrid model is tested for tracking the coronal bright points (CBPs) along time, on a set of full-disc solar images obtained with the Atmospheric Imaging Assembly (AIA) instrument, onboard the Solar Dynamics Observatory (SDO) satellite. The algorithm results are then used for determining the differential rotation of CBPs. These final results are compared with those already reported in the literature, to assess the versatility of the PSO/Snake hybrid approach.
Computer vision (or machine vision) is a field that combines image processing and
machine learning methods in order to understand the scene. Tracking is a basic task for
several applications of computer vision, e.g., motion-based recognition, automated
surveillance, video indexing, human-computer interaction, traffic monitoring, vehicle
navigation, and so on. In recent years, the research of the visual tracking has arrived at
a climax.
In the present thesis, an efficient method for object tracking is proposed using color
and texture features and two Radial Basis Function Neural Networks.
In the first frame operator selects the object region. A surrounding area is selected as
a background automatically. In the next step, the pixel-based color (Hue, Saturation,
and Value) and texture (Average, Variance and Range) features are extracted from the
object and background regions. The first RBFNN classifier is trained and tested with
these features. The first RBFNN is employed for separating object from its surrounding
background in the first frame. Color and texture features from surrounding background
are applied to develop an extended background model. The detected object and
extended background features are then used to train the second RBFNN. The trained
RBFNN is used as a scoring function that gives higher scores to the object pixels. The
second trained RBFNN is employed to detect object in subsequent frames while meanshift procedure is used to track the object location. By introducing and measuring two
criterions the object size and model are updated. The performance of the proposed
tracker is tested with many video sequences. The proposed tracker is illustrated to be
able to track object and successfully resolve the problems caused by the camera
movement, partial occlusion and gradual change in object and background color and
texture. Also the proposed method provides object region in every frame that can be
used in higher level processing such as object and activity recognition.
The main purpose of this paper is to promote the efficiency of a control system using a scheduling policy control design. In this system, the management of a computer's input and output information is handled appropriately by the program language. The scheduling policy control design is used in the robotic vision tracking system. The advantage of this control design is to activate each procedure running simultaneously when the transient overload of the information's input and output in the control system occurs. Therefore, the time run in the scheduling policy control system will be shorter than that of a traditional control system. In this paper, case studies of the scheduling police control application used in image tracking vision control are elaborated. The results reveal that the speed of the tracking system can be improved by using the scheduling policy technique under a prompt procedure plan.
An improved geodesic active contour model for the detection of object boundaries is presented. A geodesic active contour model is based on an active contour evolving in time. The geodesic active contour model has a serious weakness, that is, it stops at a local minimum where there is a deep concave boundary in an image. The generalized geodesic active contour model makes an active contour convergent to long, thin boundary indentations by introducing a shrinkage force. In this paper, we present an improved geodesic active contour model based on the generalized geodesic active contour with an error term. Experimental results show that the proposed model works successfully for concave objects, it can not only detect object boundaries, but also improve double rings. The convergence rate is faster, the robust is better than the generalized geodesic active contour model.
Active contours or snakes are widely used for segmentation and tracking. Multiple object tracking remains a difficult task, characterised by a trade off between increasing the cap- turing range of edges of the object of interest, and decreas- ing the capturing range of other edges. We propose a new external force field which is calculated for every object inde- pendently. This new force field uses prior knowledge about the intensity of the object of interest. Using this extra infor- mation, this new force field helps in discriminating between edges of interest and other objects. For this new force field, the expected intensity of an object must be estimated. We propose a technique which calculates this estimation out of the image.
Accurate monitoring of leaves and plants is a necessity for research on plant physiology. To aid this biological research,
we propose a new active contour method to track individual leaves in chlorophyll fluorescence time laps sequences. The
proposed active contour algorithm is developed such that it can handle sequences with low temporal resolution. This paper
proposes a novel optimization method which incorporates prior knowledge about the plant shape. Tests show that the
proposed framework outperforms state of the art tracking methods.
In radiotherapy treatment it is very important to find out the target organs on the medical image sequence in order to determine and apply the proper dose. The techniques to achieve this goal can be classified into extrinsic and intrinsic. Intrinsic techniques only use image processing with medical images associated to the radiotherapy
treatment, as we deal in this chapter. To accurately perform this organ tracking it is necessary to find out segmentation and tracking models that were able to be applied to several image modalities involved on a radiotherapy session (CT
, MRI
, etc.). The movements of the organs are mainly affected by two factors: breathing and involuntary movements associated with the internal organs or patient positioning. Among the several alternatives to track the organs of interest, a model based on geodesic active regions is proposed. This model has been tested over CT
images from the pelvic, cardiac, and thoracic area. A new model for the segmentation of organs composed by more than one region is proposed.
Active contours or snakes are widely used for segmentation and tracking. The ability of a snake to track an object depends on the movement of the object. If the object moves too far from one frame to another, the snake risks losing the true contour location. The subsequent evolution steps are negatively affected, reporting a false contour that can propagate to other frames. To overcome this problem a new snake algorithm has been developed. This new technique, moving snakes, works in two steps. During the first step, the snake is translated as a rigid body towards the contour. This translation is calculated using the external force field of the image, therefore it does not require prior knowledge about the object movement. In the second step the actual shape evolution of the snake takes place.
In this paper we present a novel method for tracking rigid objects (mostly vehicles) in video sequences with cluttered background, obtained from a mobile camera. We estimate the motion model of the target using corners extraction and matching together with LMedS statistical approach. The resultant model assists an active contour (snake) to track the target efficiently. To avoid tracking errors resulting from large aspect change of the target, we propose a snake with an automatic local swelling mechanism. This mechanism enables the snake to include new parts of the target which appear as a result of large aspect change. Several experiments have been conducted to show the promise of our algorithms. Key words: contour tracking , moving target , aspect change , active contour , feature matching
Methods based on deformable model have been widely used in deformable image segmentation. The segmentation quality of this kind of methods strongly relies on its initialization. If the initiation isn't accurate, the segmentation result will not be satisfactory. To solve this problem, we propose a new deformable image cascade segmentation method. In the method, the MSRF and SMAP will be used to estimate motion parameters of the background of image sequence. From the result of simulation, we can conclude that the segmentation method is satisfactory.
Active contours or snakes are widely used for segmentation and tracking. These techniques require the minimization of an energy function, which is generally a linear combination of a data fit term and a regularization term. This energy function can be adjusted to exploit the intrinsic object and image features. This can be done by changing the weighting parameters of the data fit and regularization term. There is, however, no rule to set these parameters optimally for a given application. This results in trial and error parameter estimation. In this paper, we propose a new active contour framework defined using probability theory. With this new technique there is no need for ad hoc parameter setting, since it uses probability distributions, which can be learned from a given training dataset.
Motion estimation by means of spatio-temporal energy filters –velocity tuned filters– is known to be robust to noise and aliasing
and to allow an easy treatment of the aperture problem. In this paper we propose a motion representation based on the composition
of spatio-temporal energy features, i.e., responses of a set of filters in phase quadrature tuned to different scales and
orientations. Complex motion patterns are identified by unsupervised cluster analysis of energy features. The integration
criterion reflects the degree of alignment of maxima of the features’s amplitude, which is related to phase congruence. The
composite-feature representation has been applied to motion segmentation with a geodesic active model both for initialization
and image potential definition. We will show that the resulting method is able to handle typical problems, such as partial
and total occlusions, large inter-frame displacements, moving background and noise.
Active contours or snakes are widely used for segmentation and tracking. Recently a new active contour model was proposed,
combining edge and region information. The method has a convex energy function, thus becoming invariant to the initialization
of the active contour. This method is promising, but has no regularization term. Therefore segmentation results of this method
are highly dependent of the quality of the images. We propose a new active contour model which also uses region and edge information,
but which has an extra regularization term. This work provides an efficient optimization scheme based on Split Bregman for
the proposed active contour method. It is experimentally shown that the proposed method has significant better results in
the presence of noise and clutter.
From surgery to radiotherapy treatment planning, tracking organs or tissues is a fundamental task. The techniques used to achieve this tracking can be classified as: extrinsic and intrinsic. Intrinsic techniques only use image processing methods applied to medical images or sequences, as dealt with in this paper. To accurately perform this organ tracking it is necessary to find tracking models that can be applied to various image modalities involved in medical procedures (CT, MRI, etc.). Moreover these models must handle several image dimensions (2D, 3D, and 4D) that are common in many medical tasks. Among the several alternatives for tracking the organs of interest, a model based on a geodesic one combined with regional features is proposed. This model has been tested on CT images from the pelvic, cardiac and thoracic area. A novel model for the segmentation of organs composed of more than one region is proposed.
Three-dimensional surface reconstruction of the coronary arterial from intravascular ultrasound (IVUS) images plays an important role in diagnosis and treatment of the atherosclerosis. A simplified 3-D topologically adaptable snake model (3-D T-snake) is proposed; nevertheless, it can successfully address the significant problem of conventional T-snakes when dealing with the model self-intersections. A method of surface reconstruction of IVUS coronary arterial walls based on the simplified 3-D T-snake is presented. The experimental results indicate that the method is accurate and robust, and the simplified 3-D T-Snake is feasible and valid. (c) 2007 SPIE and IS&T.
The main aim of this study is to promote the efficiency of a control system using a multithread digital control design. In this system, the management of a computer`s input and output information is handled appropriately by the program language. The multithread digital control design is used in the robotic arm`s tracking system. The advantage of this multithread digital control design is to activate each procedure running simultaneously when the transient overload of the information`s input and output in the control system occurs. Therefore, the time run in the multithread system will be shorter than that run in a traditional single thread system in which each procedure is lined up for running. In this study, case studies of multithread application used in image tracking and robot control are introduced. The results reveal that the speed of the tracking system can be improved by using the multithread technique under an immediate procedure plan.
In this paper, a new method for contour tracking of mobile target in low quality video sequence is presented. Proposed method helps to track variety of targets exactly while the camera is moving. In this study, a new type of active contour is used with a way for estimating motion model of the target. Estimating motion model of the target uses color and location information together. This information would cause our method not to be sensitive of aspect change. Our method is compared with two other methods: tracking via simple active contour and estimating motion model by using affine transform. Experimental results illustrate our method to outperform the prior ones.
In this paper an automatic trajectory tracking method is presented which combines with nodes in 3D space to structure object trajectory. The nodes are extracted frequently from two analogue cameras. A 3D orientation model is brought forward to ascertain the orientation of object centroid and predict the probabilistic location some time ahead. A 3D trajectory model is used to log object motion information, recognize object behavior pattern and classify object trajectories based on the concept of deputy object. The method is efficient in many domains, such as especial video guard against theft, video surveillance for objects in air, etc.
A novel geometric approach for three dimensional object segmentation is presented. The scheme is based on geometric deformable
surfaces moving towards the objects to be detected. We show that this model is related to the computation of surfaces of minimal
area (local minimal surfaces). The space where these surfaces are computed is induced from the three dimensional image in
which the objects are to be detected. The general approach also shows the relation between classical deformable surfaces obtained
via energy minimization and geometric ones derived from curvature flows in the surface evolution framework. The scheme is
stable, robust, and automatically handles changes in the surface topology during the deformation. Results related to existence,
uniqueness, stability, and correctness of the solution to this geometric deformable model are presented as well. Based on
an efficient numerical algorithm for surface evolution, we present a number of examples of object detection in real and synthetic
images.
Level Set Representations, the pioneering framework introduced by Osher and Sethian (14) is the most common choice for the implementation of variational frameworks in Computer Vision since it is implicit, intrinsic, param- eter and topology free. However, many Computer vision applications refer to entities with physical meanings that follow a shape form with a certain degree of variability. In this paper, we propose a novel energetic form to introduce shape constraints to level set representations. This formulation exploits all advantages of these representations resulting on a very elegant approach that can deal with a large number of parametric as well as continuous transformations. Furthermore, it can be combined with existing well known level set-based segmentation ap- proaches leading to paradigms that can deal with noisy, occluded and missing or physically corrupted data. Encouraging experimental results are obtained using synthetic and real images.
The aim of this paper is to present two kinds of shape distances in a dynamic image context. Both are defined for closed curves. The first distance is obtained by invariant descriptors, under rigid motion, which verify a completeness and stability properties. The second one distance obtained by a Hausdorff distance that allows to estimate the motion parameters. These distances are tested on real images.
A novel method for two-dimensional curve nor- malization with respect to affine transformations is presented in this paper, which allows an affine-invariant curve repre- sentation to be obtained without any actual loss of infor- mation on the original curve. It can be applied as a pre- processing step to any shape representation, classification, recognition, or retrieval technique, since it effectively decou- ples the problem of affine-invariant description from feature extraction and pattern matching. Curves estimated from ob- ject contours are first modeled by cubic B-splines and then normalized in several steps in order to eliminate translation, scaling, skew, starting point, rotation, and reflection transfor- mations, based on a combination of curve features including moments and Fourier descriptors.
Most approaches for estimating optical flow assume that, within a finite image region, only a single motion is present. This single motion assumption is violated in common situations involving transparency, depth discontinuities, independently moving objects, shadows, and specular reflections. To robustly estimate optical flow, the single motion assumption must be relaxed. This paper presents a framework based on robust estimation that addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions. We show how the robust estimation framework can be applied to standard formulations of the optical flow problem thus reducing their sensitivity to violations of their underlying assumptions. The approach has been applied to three standard techniques for recovering optical flow : area-based regression, correlation, and regularization with motion discontinuities. This paper focuses on the recovery of multiple parametric motion models within a region, as well as the recovery of piecewise-smooth flow fields, and provides examples with natural and synthetic image sequences.
We present a vision system for the 3-D model-based tracking of
unconstrained human movement. Using image sequences acquired
simultaneously from multiple views, we recover the 3-D body pose at each
time instant without the use of markers. The pose-recovery problem is
formulated as a search problem and entails finding the pose parameters
of a graphical human model whose synthesized appearance is most similar
to the actual appearance of the real human in the multi-view images. The
models used for this purpose are acquired from the images. We use a
decomposition approach and a best-first technique to search through the
high dimensional pose parameter space. A robust variant of chamfer
matching is used as a fast similarity measure between synthesized and
real edge images. We present initial tracking results from a large new
Humans-in-Action (HIA) database containing more than 2500 frames in each
of four orthogonal views. They contain subjects involved in a variety of
activities, of various degrees of complexity, ranging from the more
simple one-person hand waving to the challenging two-person close
interaction in the Argentine Tango
Snakes, or active contours, are used extensively in computer
vision and image processing applications, particularly to locate object
boundaries. Problems associated with initialization and poor convergence
to boundary concavities, however, have limited their utility. This paper
presents a new external force for active contours, largely solving both
problems. This external force, which we call gradient vector flow (GVF),
is computed as a diffusion of the gradient vectors of a gray-level or
binary edge map derived from the image. It differs fundamentally from
traditional snake external forces in that it cannot be written as the
negative gradient of a potential function, and the corresponding snake
is formulated directly from a force balance condition rather than a
variational formulation. Using several two-dimensional (2-D) examples
and one three-dimensional (3-D) example, we show that GVF has a large
capture range and is able to move snakes into boundary concavities
This paper presents a new variational framework for detecting and
tracking multiple moving objects in image sequences. Motion detection is
performed using a statistical framework for which the observed
interframe difference density function is approximated using a mixture
model. This model is composed of two components, namely, the static
(background) and the mobile (moving objects) one. Both components are
zero-mean and obey Laplacian or Gaussian law. This statistical framework
is used to provide the motion detection boundaries. Additionally, the
original frame is used to provide the moving object boundaries. Then,
the detection and the tracking problem are addressed in a common
framework that employs a geodesic active contour objective function.
This function is minimized using a gradient descent method. A new
approach named Hermes is proposed, which exploits aspects from the
well-known front propagation algorithms and compares favorably to them.
Very promising experimental results are provided using real video
sequences
A new Kalman-filter based active contour model is proposed for
tracking of nonrigid objects in combined spatio-velocity space. The
model employs measurements of gradient-based image potential and of
optical-flow along the contour as system measurements. In order to
improve robustness to image clutter and to occlusions an optical-flow
based detection mechanism is proposed. The method detects and rejects
spurious measurements which are not consistent with previous estimation
of image motion
A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours, motion tracking, and stereo matching. The authors have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.
The lack of information about the tangential velocity makes the velocity estimation erroneous in contour matching. Classical methods use the normal velocity, together with some smoothness constraints, since the tangential velocity cannot be recovered. This paper presents a contour matching method that computes displacements with a criteria of minimum curvature differences. The first derivative of tangential velocity is available from the image intensities and is related to the contour curvature. We compute the velocities using the curvature as well as the normal component. Consequentially, the estimation error due to the tangential component is reduced substantially. A contour having occluding parts leads to mismatching. Our method determines occluding parts before the contour matching by analyzing the change of curvature distribution. Experimental results showed that the proposed method computes accurate velocity vectors for various moving contours. Keywords: Contour matching; Contour...
This work investigates a new approach to the tracking of regions in an image sequence. The approach relies on two successive operations: detection and discrimination of moving targets and then pursuit of the targets. A motion-based segmentation algorithm, previously developed in the laboratory, provides the detection and discrimination stage. This paper emphaizes the pursuit stage. A pursuit algorithm has been designed that directly tracks the region representing the projection of a moving object in the image, rather than relying on the set of trajectories of individual points or segments. The region tracking is based on the dense estimation of an affine model of the motion field within each region, which makes it possible to predict the position of the target in the next frame. A multiresolution scheme provides reliable estimates of the motion parameters, even in the case of large displacements. Two interacting linear dynamic systems describe the temporal evolution of the geometry and the motion of the tracked regions. Experiments conducted on real images demonstrate that the approach is robust against occulusion and can handle large interframe displacements and complex motions. (C) 1994 Academic Press, Inc.
The fundamentals of the theory and design of systems and devices for the
digital processing of signals are presented. Particular attention is
given to algorithmic methods of synthesis and digital processing
equipment in communication systems (e.g., selective digital filtering,
spectral analysis, and variation of the signal discretization
frequency). Programs for the computer-aided analysis of digital filters
are described. Computational examples are presented, along with tables
of transfer function coefficients for recursive and nonrecursive digital
filters.
Active contour models, or "snakes," developed in (Kass et al. 1988), use a simple physical model to track edges in image sequences. Snakes as originally defined however, tend to shrink, stretch and slide back and forth in unwanted ways along a tracked edge and are also confused by multiple edges, always grabbing the nearest one. In this paper a practical solution is presented that combines motion estimation techniques with snakes to overcome these problems. An algorithm is presented that uses a block matching technique to guide the endpoints of the snake, optical flow to push the snake in the direction of the underlying motion, followed by the traditional snake edge-fitting minimization process. We use this technique for tracking facial features of an actor for driving computer animated characters.
The lack of information about tangential velocity makes velocity estimation erroneous in contour matching. Classical methods use the normal velocity, together with some smoothness constraints, since the tangential velocity cannot be recovered. This paper presents a contour matching method that computes displacements with a criteria of minimum curvature differences. The first derivative of tangential velocity is available from the image intensities and is related to the contour curvature. We compute the velocities using the curvature as well as the normal component. Consequently, the estimation error due to the tangential component is reduced substantially. A contour having occluding parts leads to mismatching. Our method determines occluding parts before the contour matching by analyzing the change of curvature distribution. Experimental results showed that the proposed method computes accurate velocity vectors for various moving contours.
This work investigates a new approach to the tracking of regions in an image sequence. The approach relies on two successive operations: detection and discrimination of moving targets and then pursuit of the targets. A motion-based segmentation algorithm, previously developed in the laboratory, provides the detection and discrimination stage. This paper emphasizes the pursuit stage. A pursuit algorithm has been designed that directly tracks the region representing the projection of a moving object in the image, rather than relying on the set of trajectories of individual points or segments. The region tracking is based on the dense estimation of an affine model of the motion field within each region, which makes it possible to predict the position of the target in the next frame. A multiresolution scheme provides reliable estimates of the motion parameters, even in the case of large displacements. Two interacting linear dynamic systems describe the temporal evolution of the geometry and the motion of the tracked regions. Experiments conducted on real images demonstrate that the approach is robust against occlusion and can handle large interframe displacements and complex motions.
This paper presents a framework for detecting and tracking moving
objects in a sequence of images. Using a statistical approach, where the
inter-frame difference is modeled by a mixture of two Laplacian or
Gaussian distributions, and an energy minimization based approach, we
reformulate the motion detection and tracking problem as a front
propagation problem. The Euler-Lagrange equation of the designed energy
functional is first derived and the flow minimizing the energy is then
obtained. Following the work by Caselles et al. (1995) and Malladi et
al. (1995), the contours to be detected and tracked are modeled as
geodesic active contours evolving toward the minimum of the designed
energy, under the influence of internal and external image dependent
forces. Using the level set formulation scheme of Osher and Sethian
(1988), complex curves can be detected and tracked and topological
changes for the evolving curves are naturally managed. To reduce the
computational cost required by a direct implementation, of the
formulation scheme of Osher and Sethian (1988), a new approach
exploiting aspects from the classical narrow band and fast marching
methods is proposed and favorably compared to them. In order to further
reduce the CPU time, a multi-scale approach has also been considered.
Very promising experimental results are provided using real video
sequences
Energy-minimising active contour models based on dynamic
programming have been proposed by Amini et al. (1988) as a discrete
multi-stage decision process. The behaviour of the active contour is
generally controlled by its internal and external energies. Internal
energy is composed of two parts; the first part, acts to shorten the
active contour as it iterates towards the interest object, while the
second part is the curvature of the active contour and forces smoothness
of active contour during its movement towards interest object. In Amini
et al., the three points i, i-1 and i-2 were used to estimate the
curvature of the active contour at point i. Also the external energy of
active contour at this point was calculated as the distance from its
previous point to the nearest edge of underlying image. Then due to both
of these problems, locking on to interest object does not occur very
accurately especially at some points on the boundary of object where
curvature changes very quickly. In this paper a reformulated internal
energy is proposed to improve the computation of curvature at point i by
making use of the three points i-1, i and i+1. Furthermore, external
energy of active contour at any point is defined as its distance to
nearest edge of underlying image Consequently our proposed active
contour model can lock on to interest objects more accurately using the
same snake parameters and initial position. Images with single and
multiple objects are selected to evaluate the capability of our proposed
method. The results show that locking on to interest objects occurs
completely like a membrane or thin plate
In this paper, we review the recently published work on deformable models. We have chosen to concentrate on 2D deformable models and relate the energy minimization approaches to the Bayesian formulations. We categorize the various active contour systems according to the definition of the deformable model. We also present in detail one particular formulation for deformable templates which combines edge, texture, color and region information for the external energy and model deformations using wavelets, splines or Fourier descriptors. We explain how these models can be used for segmentation, image retrieval in a large database and object tracking in a video sequence.
Active contour models (“snakes”) are a powerful tool for deformable object tracking in moving images. But the existing snake models are not well-adapted for tracking corners and objects on a complex background. In this paper, we present a novel active contour model, the “Adjustable Polygons”, which is a set of active segments that can fit any object shape (including comers). A new energy based on textural characteristics of objects is also proposed, in order to resolve conflict situations while tracking objects on multiple contour background.
We propose an efficient method for tracking several objects moving through a sequence of monocular images against a non-uniform background. Each object entering the scene is intercepted by an active contour model which locks on it as long as it moves in the scene. The procedure does not necessitate an interactive initialization. Some results are presented in case of real traffic scenes.
In this paper, we show that existing shaped-based active contour models are not affine-invariant and we addressed the problem by presenting an affine-invariant snake model (AI-snake) such that its energy function are defined in terms local and global affine-invariant features. The main characteristic of the AI-snake is that, during the process of object extraction, the pose of the model contour is dynamically adjusted such that it is in alignment with the current snake contour by solving the snake-prototype correspondence problem and determining the required affine transformation. In addition, we formulate the correspondence matching between the snake and the object prototype as an error minimization process between two feature vectors which capture both local and global deformation information. We show that the technique is robust against object deformations and complex scenes.
The use of energy-minimizing curves, known as “snakes,” to extract features of interest in images has been introduced by Kass, Witkin & Terzopoulos (Int. J. Comput. Vision 1, 1987, 321–331). We present a model of deformation which solves some of the problems encountered with the original method. The external forces that push the curve to the edges are modified to give more stable results. The original snake, when it is not close enough to contours, is not attracted by them and straightens to a line. Our model makes the curve behave like a balloon which is inflated by an additional force. The initial curve need no longer be close to the solution to converge. The curve passes over weak edges and is stopped only if the edge is strong. We give examples of extracting a ventricle in medical images. We have also made a first step toward 3D object reconstruction, by tracking the extracted contour on a series of successive cross sections.
This paper presents a novel method of velocity field estimation for the points on moving contours in a 2-D image sequence. The method determines the corresponding point in a next image frame by considering the curvature change of a given point on the contour. In traditional methods, there are errors in optical flow estimation for the points which have low curvature variations since those methods compute solutions by approximating normal optical flow. The proposed method computes optical flow vectors of contour points minimizing the curvature changes. As a first step, snakes are used to locate smooth curves in 2-D imagery. Thereafter, the extracted curves are tracked continuously. Each point on a contour has a unique corresponding point on the contour in the next frame whenever the curvature distribution of the contour varies smoothly. The experimental results showed that the proposed method computes accurate optical flow vectors for various moving contours.
We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front also can be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand sides, by using techniques from hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general Hamilton-Jacobi-type problems. We demonstrate our algorithms by computing the solution to a variety of surface motion problems.
A snake is an energy-minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Scale-space continuation can be used to enlarge the capture region surrounding a feature. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and subjective contours; motion tracking; and stereo matching. We have used snakes successfully for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest.
Active contour models, or “snakes,” developed in (Kass et al. 1988), use a simple physical model to track edges in image sequences. Snakes as originally defined however, tend to shrink, stretch and slide back and forth in unwanted ways along a tracked edge and are also confused by multiple edges, always grabbing the nearest one. In this paper a semi-automatic system is presented that combines motion estimation techniques with snakes to overcome these problems. An algorithm is presented that uses a block matching technique to guide the endpoints of the snake, optical flow to push the snake in the direction of the underlying motion, followed by the traditional snake edge-fitting minimization process. We use this technique for tracking facial features of an actor for driving computer animated characters.
. In Proc. European Conf. Computer Vision, 1996, pp. 343--356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm --- Conditional Density Propagation over time. It uses `factored sampling', a method previously applied to interpretation of static images, in which the distribution of possible interpretations is represented by a randomly generated set of representatives. The Condensation algorithm combines factored sampling with learned dynamical models to propagate an entire probability distribution for object pos...
We present a vision system for the 3-D modelbased tracking of unconstrained human movement. Using image sequences acquired simultaneously from multiple views, we recover the 3-D body pose at each time instant without the use of markers. The poserecovery problem is formulated as a search problem and entails finding the pose parameters of a graphical human model whose synthesized appearance is most similar to the actual appearance of the real human in the multi-view images. The models used for this purpose are acquired from the images. We use a decomposition approach and a best-first technique to search through the high dimensional pose parameter space. A robust variant of chamfer matching is used as a fast similarity measure between synthesized and real edge images. We present initial tracking results from a large new Humans-In-Action (HIA) database containing more than 2500 frames in each of four orthogonal views. They contain subjects involved in a variety of activities, of various de...
This papers presents a framework for detecting and tracking moving objects in a sequence of images. Using a statistical approach, where the inter-frame dioeerence is modeled by a mixture of two Laplacian or Gaussian distributions, and an energy minimization based approach, we reformulate the motion detection and tracking problem as a front propagation problem. The Euler-Lagrange equation of the designed energy functional is rst derived and the flow minimizing the energy is then obtained. Following the work by Caselles et al [11] and Malladi et al [23, 24] the contours to be detected and tracked are modeled as geodesic active contours evolving toward the minimum of the designed energy, under the influence of internal and external image dependent forces. Using the level set formulation scheme of Osher and Sethian [29], complex curves can be detected and tracked and topological changes for the evolving curves are naturally managed. To reduce the computational cost required by a direct i...
We address the problem of fast computation of level set partial differential equations (PDEs) in the context of motion segmentation. Although several fast level set computation algorithms are known, some of them, such as the fast marching method, are not applicable to the video segmentation problem since the front being computed does not advance monotonically. We study narrow-banding, pyramidal and a pyramidal/narrow-banding schemes that leads to a 70-fold time gain over the single-resolution scheme. 1.
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields Minimal surfaces: a geometric three dimensional segmentation approach
- M J Black
- P Anandan
- V Caselles
- R Kimmel
- G Sapiro
- C Sbert
M.J. Black, P. Anandan, The robust estimation of multiple motions: parametric and piecewise-smooth flow fields, CVIU 63 (1) (1996) 75–104. [3] V. Caselles, R. Kimmel, G. Sapiro, C. Sbert, Minimal surfaces: a geometric three dimensional segmentation approach, Numer. Math. 77 (4) (1997) 423–425.
Macmillan College Publish-ing Company Object tracking with insufficient motion field and partial occlusion
- S Haykin
- Neural
S. Haykin, Neural networks, Macmillan College Publish-ing Company, New York, 1994, pp. 124–126 (Section 5.3). Recogn.16 (1995) ARTICLE IN PRESS Fig. 14. Object tracking with insufficient motion field and partial occlusion. G. Tsechpenakis et al. / Signal Processing: Image Communication 19 (2004) 219–238 237
Object tracking with insufficient motion field and partial occlusion
- Article In
- Fig
ARTICLE IN PRESS
Fig. 14. Object tracking with insufficient motion field and partial occlusion.
Dubuisson-Jolly, Deform-able template models: a review, Signal Process
- A K Jain
- Y Zhong
A.K. Jain, Y. Zhong, M.-P. Dubuisson-Jolly, Deform-able template models: a review, Signal Process. 71 (1998) 109–129.
Computer Vision Textbook from the post-graduate course " Computer Vision
- P Maragos
P. Maragos, " Computer Vision, " Textbook from the post-graduate course
" Computer Vision " in Electrical & Computer Eng. Dept. of National Technical
University of Athens, instructor P. Maragos, chapt. 5, 2002.
3-D Model-based tracking of humans in action: a multi-view approach
- D Gravila
- L Davis
D. Gravila, L. Davis, 3-D Model-based tracking of
humans in action: a multi-view approach, in: Proceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR'96), San Francisco, CA, 1996,
pp. 73-80.
Snakes, shapes, and gradient vector flow
- Xu
C. Xu, J.L. Prince, Snakes, shapes, and gradient vector
flow, IEEE Trans. Image Processing 7 (3) (1998) 359-369.
The robust estimation of multiple motions
- Black
Deformable template models
- Jain