Tracking strategies usually employ motion and appearance models to locate observations of the tracked object in successive frames. The subsequent model update procedure renders the approach highly sensitive to the inevitable observation and occlusion noise processes. In this work, two robust mechanisms are proposed which rely on knowledge about the ground plane. First a highly constrained bounding box appearance model is proposed which is determined solely from predicted image location and visual motion. Second, tracking is performed on the ground plane enabling global real-world observation and dynamic noise models to be defined. Finally, a novel auto-calibration procedure is developed to recover the image to ground plane homography by simply accumulating event observations.
The problem of colour constancy in the context of visual surveillance applications is addressed in this paper. We seek to reduce the variability of the surface colours inherent in the video of most indoor and outdoor surveillance scenarios to improve the robustness and reliability of applications which depend on reliable colour descriptions e.g. content retrieval. Two well-known colour constancy algorithms -the Grey-World and Gamut-Mapping -are applied to frame sequences containing significant variations in the colour temperature of the illuminant. We also consider the problem of automatically selecting a reference image, representative of the scene under the canonical illuminant. A quantitative evaluation of the performance of the colour constancy algorithms is undertaken.
Complex scenes such as underground stations and malls are composed of static occlusion structures such as walls, entrances, columns, turnstiles and barriers. Unless this occlusion landscape is made explicit such structures can defeat the process of tracking individuals through the scene. This paper describes a method of generating the probability density functions for the depth of the scene at each pixel from a training set of detected blobs, i.e., observations of detected moving people. As the results are necessarily noisy, a regularization process is employed to recover the most self-consistent scene depth structure. An occlusion reasoning framework is proposed to enable object tracking methodologies to make effective use of the recovered depth.
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