Thereafter, based on the extracted foreground masks, we present a new Marked Point Process (MPP)-based method for pedestrian localization and height estimation in multi-camera systems, and give a detailed comparative evaluation of the proposed method versus a state-of-the-art technique. For the camera image sequences, we propose first a Markov Random Field (MRF)-based foreground extraction technique which is able to address cast shadow detection and the exploitation of spatial coherence of the color and texture values observed in the foreground regions. Two different sensors are used for these tasks: conventional electro-optical video cameras, and Rotating Multi-Beam (RMB) Lidar sensors. In this chapter, we discuss Bayesian approaches for foreground object detection and localization in video surveillance applications.
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