An integrated framework for intelligent people surveillance using multiple cameras is proposed in this thesis. The cameras are geometrically related through joint efforts of multi-camera calibration, ground plane homography constraint, and field-of-view lines. As a result, they work in a cooperative manner under passive scene view mode or active object view mode. Under the passive mode, object detection is implemented using an adaptive Gaussian mixture model. Based on a study of multi-view object association, the detected objects are consistently labeled and projected onto a virtual top-view of ground plane. Under the active mode, a color-based particle filtering algorithm is utilized to constantly track an object. A novel relay strategy is developed by combining the tracking algorithm and the pan-tilt-zoom capabilities, and therefore the object being tracked can be handed over between cameras. Finally, a preliminary study is also carried out on anomaly detection and localization via wearable wireless motion sensors.
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Thesis advisor: Payandeh, Shahram
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