Bayesian Models for Dynamic Scene Analysis

In this chapter, we discuss Bayesian approaches for foreground object detection and localization in video surveillance applications. Two different sensors are used for these tasks: conventional electro-optical video cameras, and Rotating Multi-Beam (RMB) LidarLight Detection and Ranging (Lidar) sensors. For the camera image sequences, we propose first a Markov Random Field (MRF)Markov Random Field (MRF)-based foreground extraction technique which is able to address cast shadow detectionShadow detection and the exploitation of spatial coherence of the color and texture values observed in the foreground regions. Thereafter, based on the extracted foreground masks, we present a new Marked Point Process (MPP)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. The last part of the chapter deals with LidarLight Detection and Ranging (Lidar) point cloud processing where key challenges are compensating the low and inhomogeneous spatial resolution of the measurements, and various artifacts in point cloud formation caused by the rotating sensor technology. We also present here application examples including motionMotion detection detection, gait-based pedestrian re-identificationRe-identification and activity recognition using a single RMB LidarRotating Multi-beam Lidar (RMB Lidar) sensor which monitors the scene from a fixed position.