Coarse-to-Fine Multi-camera Network Topology Estimation

In multiple camera networks, the correlation of multiple cameras can provide us with a richer information than a single camera. In order to make full use of the association information between multiple cameras. We propose a novel approach to estimate a camera topology relationship in a multi-camera surveillance network, which is unsupervised and gradually refined from coarse to fine. First, an improved cross-correlation function is used to get a preliminary result, then a time constraint feature matching model is used to reduce the error caused by external environment and noise, which can increase the accuracy of our results. Finally, we test the proposed method on several different datasets, and its result indicates that our approach perform well on recovering the topology of the camera and can improve the accuracy on over camera tracking.