Inference topology of distributed camera networks with multiple cameras

This paper proposes an inference method to construct the topology of a camera network with overlapping and non-overlapping fields of view for a commercial surveillance system equipped with multiple cameras. It provides autonomous object detection, tracking and recognition in indoor or outdoor urban environments. The camera network topology is estimated from object tracking results among and within FOVs. The merge-split method is used for object occlusion in a single camera and an EM-based approach for extracting the accurate object feature to track moving people and establishing object correspondence across multiple cameras. The appearance of moving people and the transition time between entry and exit zones is measured to track moving people across blind regions of multiple cameras with non-overlapping FOVs. Our proposed method graphically represents the camera network topology, as an undirected weighted graph using the transition probabilities and 8-directional chain code. The training phase and the test were run with eight cameras to evaluate the performance of our method. The temporal probability distribution and the undirected weighted graph are shown in the experiments.