TY - JFULL AU - Yunyoung Nam and Junghun Ryu and Yoo-Joo Choi and We-Duke Cho PY - 2007/7/ TI - Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People T2 - International Journal of Computer and Information Engineering SP - 1548 EP - 1554 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/7797 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 6, 2007 N2 - This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities. ER -