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Scene camera inside the vehicle. The picture is taken from the driving seat, and the camera is located between the driver and passenger seats.
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This paper presents a system for the search and detection of moving objects in a sequence of images previously captured by a camera installed in a conventional vehicle. The objective is the design and implementation of a software system based on optical flow analysis to detect and identify moving objects as perceived by a driver, taking into accoun...
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Although people or object tracking in uncontrolled environments has been acknowledged in the literature, the accurate localization of a subject with respect to a reference ground plane remains a major issue. This study describes an early prototype for the tracking and localization of pedestrians with a handheld camera. One application envisioned he...
A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-base...
Citations
... It gives a dense correspondence between frames, but at the cost of being limited to rigid objects, and computation entangled. Additionally, optical flow can only work within scenes where the movement of the camera is significantly lower than the movement of the object [36,37,40]. This can be seen as a limitation, as the background subtraction method can be used in a wider range of applications. ...
... Another segmentation approach is based on the detection of visual motion. It is based on the fact that moving objects in the scene induce consistent changes to the flow of pixels in a region [37,40]. However, due to substantial displacements or occlusions, their calculated optical flow may contain considerable inaccuracies [43][44][45]. ...
Tracking fish movements and sizes of fish is crucial to understanding their ecology and behaviour. Knowing where fish migrate, how they interact with their environment, and how their size affects their behaviour can help ecologists develop more effective conservation and management strategies to protect fish populations and their habitats. Deep learning is a promising tool to analyse fish ecology from underwater videos. However, training deep neural networks (DNNs) for fish tracking and segmentation requires high-quality labels, which are expensive to obtain. We propose an alternative unsupervised approach that relies on spatial and temporal variations in video data to generate noisy pseudo-ground-truth labels. We train a multi-task DNN using these pseudo-labels. Our framework consists of three stages: (1) an optical flow model generates the pseudo-labels using spatial and temporal consistency between frames, (2) a self-supervised model refines the pseudo-labels incrementally, and (3) a segmentation network uses the refined labels for training. Consequently, we perform extensive experiments to validate our method on three public underwater video datasets and demonstrate its effectiveness for video annotation and segmentation. We also evaluate its robustness to different imaging conditions and discuss its limitations.
... The key technologies that are applied in autonomous vehicles include recognition of the driving environment on behalf of the driver, detection of the vehicle position during driving, detection and recognition of objects on the road (such as other vehicles or pedestrians), and implementation of devices and components required for vehicle steering. Currently, advanced driver-assistance system (ADAS) technologies are the subject of continuous study, and have been developed to recognize information relating to various surroundings and objects on the road [6][7][8][9]. ADAS technology is one of the key technologies in autonomous vehicles for lane, road, vehicle, pedestrian, traffic light, and obstacle recognition. In particular, in analyzing the driving situation of vehicles, assisting the driver, determining distances to other moving vehicles, determining the motion direction of vehicles, and determining vehicle position in a lane are important. ...
Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In order to support safe driving of autonomous vehicles, it is necessary to drive the vehicle with the VP in center of the road image in order to prevent the vehicle from moving out of the road area while driving. Accordingly, in order to detect the VP in the road image, a method of detecting a point where straight lines intersect in an area where edge directional feature information is concentrated is required. The visual attention model and image segmentation process are applied to quickly identify candidate VPs in the area where the edge directional feature-information is concentrated and the intensity contrast difference is large. In the proposed method, VPs are detected by analyzing the edges, visual-attention regions, linear components using the Hough transform, and image segmentation results in an input image. Our experimental results have shown that the proposed method could be applied to safe-driving support systems.
... For instance, in [17], one of the pioneers approaches known as Lukas-Kanade studies the cost effectiveness of the image registration techniques used for motion analysis and proposes to use the spatial intensity information to estimate the pixel that generates the best matching score in the images. Consequently, some algorithms also try to overcome those facts by the inclusion of hardware [18] or by the estimation based on the theory of warping [19], a pyramidal implementation of images [20], or by smoothing using robust kernels [21]. ...
Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.
... Acquiring three-dimensional (depth) information by using a camera system has attracted attention in the field of image-sensing applications, such as object identification by in-vehicle camera (Antoio et al. 2014) and machine vision applications (Karaoguz et al. 2014;Han et al. 2014;Liang et al. 2014). The light field camera, where microlens array is employed (Ng et al. 2005;Nussbaum et al. 1997), is one of solutions to record such imagery information. ...
This paper focuses on the compressive stress reduction in a SiO2 optical window. The design and fabrication of the optical window for an optical modulator toward image sensing applications are reported. The optical window consists of micrometer-order SiO2 capillaries (porous solids) that can modulate the transmission light intensity by moving a liquid in and out of the porous solid. A high optical transmittance can be achieved due to refractive index matching when the liquid is penetrated into the porous solid. Otherwise, its light transmittance is low because of light reflection and scattering by air holes and capillary walls. Silicon capillaries fabricated by deep reactive ion etching process are completely oxidized to form the SiO2 capillaries. A large compressive stress of the oxide causes bending of the capillary structure, which is reduced by using thin supporting beams. A 7.2 mm × 9.6 mm optical window area toward a fully integrated with the image sensor is successfully fabricated, and a light modulation effect dependent on liquid penetration is clearly demonstrated in visible region (wavelength range from 450 to 650 nm).
... as a replacement for human vision, but also handles invisible and multidimensional information (Antoio et al. 2014;Karaoguz et al. 2014;Han et al. 2014;Liang et al. 2014). ...
This paper reports the fabrication of Tempax glass capillaries based on a glass reflow into nano-trench for an optical modulator toward image sensing applications. The optical window consists of micrometer-order glass capillaries (porous solids) that can modulate the transmission light intensity by moving a liquid in and out of the porous solids. A high optical transmittance can be achieved due to refractive index matching when the liquid is penetrated into the porous solid. Otherwise, its light transmittance is low because of light reflection and scattering by air holes and capillary walls. The glass is completely filled into the nano-trench between silicon pillars under a high temperature process and assistance of enhancement of the surface wettability. Glass capillaries with depth of 8 μm, diameter of 1.2 μm, and the pitch of two capillaries of 2 μm have been achieved. The optical window integrated with an image sensor for an optical modulator is clearly demonstrated and a light modulation effect dependent on liquid penetration is observed.
The paper presents the recursive-search based algorithm f determining the displacements of surface patch (optical flow) to be applied on GPU utilizing the parallel computations principle. OpenCL computing language is used to implement the parallel computations algorithm which is focused on application on AMD Radeon GPUs. The results of testing of the proposed algorithm show the multiple decreases the time of displacement vector field construction while maintaining the accuracy and robustness.
A convolution neural network (ConvNet) based vehicle detection system is developed in view of this issue that vehicle detection based on monocular vision is susceptible to be disturbed by complex background scene. Firstly, in order to detect shadows underneath vehicles for generating the candidate regions of shadow underneath vehicle, a road detection method using edge enhancement as well as an adaptive shadow segmentation approach are applied, which are aimed to better deal with the problems of grayscale variation on road and reduce the impact of the lighting variance. Then the ConvNet's structure applied to the road traffic environment is determined and trained by the established image sample sets. The shadow regions detected wrongly as the shadows underneath vehicles are recognized by ConvNet and removed from the preliminary detection results so as to precisely verify the presence of vehicles in an image. The experimental results indicate that this algorithm described in this paper is effective and precise, which can distinguish well between vehicles and background interferences.
This paper reports the design and fabrication of a 7.2 mm × 9.6 mm freestanding compressive stress SiO2 optical window without buckling. An application of the SiO2 optical window with and without liquid penetration has been demonstrated for an optical modulator and its optical characteristic is evaluated by using an image sensor. Two methods for SiO2 optical window fabrication have been presented. The first method is a combination of silicon etching and a thermal oxidation process. Silicon capillaries fabricated by deep reactive ion etching (deep RIE) are completely oxidized to form the SiO2 capillaries. The large compressive stress of the oxide causes buckling of the optical window, which is reduced by optimizing the design of the device structure. A magnetron-type RIE, which is investigated for deep SiO2 etching, is the second method. This method achieves deep SiO2 etching together with smooth surfaces, vertical shapes and a high aspect ratio. Additionally, in order to avoid a wrinkling optical window, the idea of a Peano curve structure has been proposed to achieve a freestanding compressive stress SiO2 optical window. A 7.2 mm × 9.6 mm optical window area without buckling integrated with an image sensor for an optical modulator has been successfully fabricated. The qualitative and quantitative evaluations have been performed in cases with and without liquid penetration.
