In this paper, we propose an unsupervised approach for learning the three factors of the topology of a non-overlapping multi- camera network, which are nodes, links, and transition time distributions. It is a cross-correlation based method. Different from previous methods, the proposed method can deal with large amounts of data without con- sidering the size of time window. The connectivity between nodes is estimated based on the N-neighbor accumulated cross-correlations, as well as the transition time distribution for each link. Furthermore, inte- grated with similarity cues, the proposed method can be extended into weighted cross-correlation models for better performance. Experimental results both on simulated and real-life datasets demonstrate the effec- tiveness of the proposed method.
['Xiaotang Chen', 'Kaiqi Huang', 'Tieniu Tan']
Learning the Three Factors of a Non-overlapping Multi-camera Network Topology