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This paper proposes a video-surveillance system based on a mobile
camera. In particular the developed system creates (during the off-line
phase) a panoramic multilayer background image allowing one to use
common change detection algorithms to search for a change detection
binary image. Different approaches to get the change detection images
are pre...
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Citations
... [55]). Anyway many standard methods such as old fashioned background subtraction for change detection cannot be employed in these circumstances, unless an accurate off-line training phase is performed [151]. ...
The emergence of new pervasive wearable technologies such as action cameras and smart glasses, brings the focus of Computer Vision research to the so called First Person Vision (FPV), or Egocentric Vision. Nowadays, more and more everyday-life videos are being shot from a first-person point of view, overturning the classical fixed-camera understanding of Vision, specializing the existing knowledge of video processing from moving cameras and bringing new challenges in the field. The trend in research is already oriented towards a new type of Computer Vision, centred on moving sensors and driven by the need for new applications for smart wearable devices. More in detail, the simple realization that we often look at our hand, even while performing the simplest tasks in everyday life, motivates recent studies in hand-related inference methods.
Indeed, this thesis investigates hand-related methods, as a way for providing new functionalities to wearable devices. Inspired by a detailed state-of-the-art investigation, a unified hierarchical structure is proposed, that optimally organizes processing levels to reduce the computational cost of the system and improve its performance. Such structure borrows some concepts from the theory of Cognitive Dynamic Systems. The central body of the thesis consists then in a proposed approach for most of the levels sketched in the global framework proposed.
... [28] [29] [30] [31] [32] ...
The emergence of new pervasive wearable technologies (e.g. action cameras and smart glasses) calls attention to the so called First Person Vision (FPV). In the future, more and more everyday-life videos will be shot from a first-person point of view, overturning the classical fixed-camera understanding of
Vision, specializing the existing knowledge of moving cameras and bringing new challenges in the field of video processing. The trend in research is going to be oriented towards a new type of computer vision, centred on moving sensors and driven by the need for new applications for wearable devices. We identify
hand tracking and gesture recognition as an essential topic in this field, motivated by the simple realization that we often look at our hand, even while performing the simplest tasks in everyday life. In addition, the next frontier in user interfaces are hands-free devices. In this work we argue that applications based on FPV may involve information fusion at various complexity and abstraction levels, ranging from pure image processing to inference over patterns. We address the lowest, by proposing a first investigation on hand detection from a first-person point of view sensor and some preliminary results obtained fusing colour and optic flow information.
... Conditionally , the background scene and the viewing sensor are required to be stationary when background subtraction is applied. Recently, motion detection with a non-stationary viewing sensor has attracted the attention of several research groups [3, 7, 1, 10] . The applications include vehicleborne or airborne video surveillance, object detection and tracking with a pan-tilt camera and others. ...
This paper proposes a new background subtraction method for detecting moving objects (foreground) from a time-varied background. While background subtraction has traditionally worked well for stationary backgrounds, for a non-stationary viewing sensor, motion compensation can be applied but is difficult to realize to sufficient pixel accuracy in practice, and the traditional background subtraction algorithm fails. The problem is further compounded when the moving target to be detected/tracked is small, since the pixel error in motion compensating the background will subsume the small target. A Spatial Distribution of Gaussians (SDG) model is proposed to deal with moving object detection under motion compensation that has been approximately carried out. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is classified as belonging to the foreground or background. For this system to perform under lighting and environmental changes over an extended period of time, the background distribution must be updated with each incoming frame. A new background restoration and adaptation algorithm is developed for the time-varied background. Test cases involving the detection of small moving objects within a highly textured background and a pan-tilt tracking system based on a 2D background mosaic are demonstrated successfully.
... Change detection methods applied to images of the same scene captured by a fixed camera is well studied [17], but there are few work on change detection of scene in temporally separated videos taken from a mobile camera. In [13] , a video surveillance system based on pan/tilt mobilecamera is presented. In this case, the background image and the camera position are used to perform change detection , but the system fails to detect changes when the camera position is unknown. ...
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... Normally, the images to be compared are acquired at the same period of the year and on the same time of the day to avoid the effect of the shadows coming from the sun light. Change detection techniques are also widely used in video surveillance [6]. In most cases, the camera is static or only rotating around its optical center. ...
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... • Determination of a sensing strategy based on the overall goal of the active-vision system. For example, in [7] and [11] active vision was used only as a way to eliminate the workspace-size/pixel-resolution tradeoffs usually encountered with the use of single, staticcamera surveillance systems. The control strategy adjusts the external camera parameters (camera orientation) to monitor the entire workspace and acquire high-resolution images of a detected target. ...
In this paper, a novel reconfigurable surveillance system that incorporates multiple active-vision sensors is presented. The proposed system has been developed for visual-servoing and other similar applications, such as tracking and state estimation, which require accurate and reliable target surveillance data. In the specific implementation case discussed herein, the position and orientation of a single target are surveyed at predetermined time instants along its unknown trajectory. Dispatching is used to select an optimal subset of dynamic sensors, to be used in a data-fusion process, and maneuver them in response to the motion of the object. The goal is to provide information of increased quality for the task at hand, while ensuring adequate response to future object maneuvers. Our experimental system is composed of a static overhead camera to predict the object's gross motion and four mobile cameras to provide surveillance of a feature on the object (i.e., target). Object motion was simulated by placing it on an xy table and preprogramming a path that is unknown to the surveillance system. The selected cameras are independently and optimally positioned to estimate the target's pose (a circular marker in our case) at the desired time instant. The target data obtained from the cameras, together with their own position and bearing, are fed to a fusion algorithm, where the final assessment of the target's pose is determined. Experiments have shown that the use of dynamic sensors, together with a dispatching algorithm, tangibly improves the performance of a surveillance system
... The aim of this configuration is to have a system capable of monitoring a scene with a wide field of view, extracting salient information of the moving object and then focus the attention (acquiring frames with higher resolution) through a second mobile camera. The first part of the chain of modules on PC1 (connected to the static camera) reflects the scheme of a classical Video Surveillance systems, in which low-level representation modules operate at pixel level grabbing images from the camera ("Acquisition module"), evaluating difference images ("Change Detection module") based on a background image dynamically updated [15] and performing some morphological filtering in order to enhance image quality. The "Blob Coloring" module in PC1 processing chain follows the "Change Detection" module and acts as interface between low-level Image Processing modules and interpretation, high-level representation modules [16] providing a synthetic representation of region of amorphous pixels by using bounding boxes. ...
Several advanced video-surveillance systems based on video processing and understanding techniques have been recently developed [1, 2]. The principal aim of such systems is to recognize and classify potentially dangerous situations and consequentially generate some kind of alarm to raise the attention of a human operator. The use of automatic scene understanding systems is becoming more and more frequent in modern society: in particular, video-surveillance systems can be used for transport monitoring [3, 4], urban and building security [5], tourism [6], and bank protection [7, 8], even if their use was originally restricted to a military related field [9, 10]. Fast improvements in computing capabilities, cheap sensors and advanced image processing algorithms can be considered as the enabling technologies for the development of real-time video surveillance and monitoring systems. In particular, aspects related to the distribution of intelligence in cooperative systems need to be considered for the development of third-generation surveillance systems. A multiple sensors setup can be useful for satisfying several requirements on the system functionalities: a system using several video sensors without overlapped fields of view can be useful when a large area needs to be guarded.
... In [17], an image mosaic is constructed for the panoramic multilayer background image allowing one to use common change detection algorithms to search for change detection. Three different change detection techniques using the multilayer background: mean criterion, minimum difference criterion and minimum distance criterion are proposed for change detection for a mobile camera. ...
Due to the decreasing costs and increasing miniaturization of video cameras, the use of digital video based surveillance as a tool for real-time monitoring is rapidly increasing. In this paper, we present a new methodology for real-time video surveillance based on Experiential Sampling. We use this framework to dynamically model the evolving attention in order to perform efficient monitoring. We exploit the context and past experience information in order to detect and track moving objects in surveillance videos. Moreover, we take the situation of multiple surveillance cameras into account and utilize the experiential sampling technique to decide which surveillance video stream to be displayed on the main monitor. This can tremendously help in reducing the manual operator fatigue for multiple monitor situation. We have implemented the developed algorithms and experimental results have been presented to illustrate the utility of the proposed technique.
... These techniques are computationally heavy and the realization of a real-time video-surveillance system based on such algorithm on a multi-purpose computer architecture is difficult at the current stage of technology. A method is shown for extending efficient algorithms already developed and tested for fixed cameras to a mobile camera environment [31]. The mobile camera acquires a certain number of images of the empty scene with different position of its head. ...
In the last decade video-surveillance systems have been developed for guarding remote environments in order to detect and prevent dangerous situations. Until few years ago, surveillance was performed entirely by human operators, who interpreted the visual information presented to them on one or more monitors. Sometimes, their work conditions resulted in failing to raise the generation of alarms in case of dangerous situations.
... The aim of this configuration is to have a system capable of monitoring a scene with a wide field of view, extracting salient information of the moving object and then focus the attention (acquiring frames with higher resolution) through a second mobile camera. The first part of the chain of modules on PC1 (connected to the static camera) reflects the scheme of a classical Video Surveillance systems, in which low-level representation modules operate at pixel level grabbing images from the camera ( " Acquisition module " ), evaluating difference images ( " Change Detection module " ) based on a background image dynamically updated [15] and performing some morphological filtering in order to enhance image quality. The " Blob Coloring " module in PC1 processing chain follows the " Change Detection " module and acts as interface between low-level Image Processing modules and interpretation, high-level representation modules [16] providing a synthetic representation of region of amorphous pixels by using bounding boxes. ...
This paper describes a cooperative distributed system for outdoor surveillance based on fixed and mobile cameras. In order to continuously monitor the entire scene, a fixed unidirectional sensor has been used, mounted on the roof of a building in front of the guarded area. To obtain higher resolution images of a particular region in the scene, an active pan-tilt-zoom camera has been used. The low resolution images are used to detect and locate moving objects in a scene. The estimated object position is used in order to evaluate pan-tilt movements that are necessary in order to focus the attention of the mobile-head camera on the considered object at a higher zoom level. The system is able to provide automatic change detection at multiple zoom levels. A video shot with a small zoom factor is used to monitor the entire scene from a fixed camera, while a medium and high zoom factor are used to improve interpretation of the scene. The use of a mobile camera allows one to exceed the limitations of the bounded field of view of the sensor imposed by a fixed camera. In this case it is not possible to provide a priori knowledge about the background of the scene. The proposed method for solving the non-fixed background problem for mobile cameras involves realization of a multilevel structure obtained by the acquisition of several images. The panoramic image of the whole scene is generated using a mosaicing technique. Both sensors are used to detect and estimate the precise location of a given object at different zoom levels in order to obtain a better position estimation.
