People Movement Linkage Based on Path Revision Across Multiple Cameras

Abstract

This work presents an object-based people motion linkage system, which tracks and records the behavior of each person across multiple cameras. The proposed system includes two phases: path construction phase and path revision phase. The spatiotemporal relationships among cameras are trained by batch-learning procedures, and the appearance model is improved by color calibration for different cameras. When a person moves across cameras, the spatiotemporal relationship and appearance model are used to obtain the correspondence. However, object tracking may be lost due to some unexpected events. We revise the tracking paths by using the backward tracking technique to connect the missed links. Moreover, the trained Hidden Markov Models are further used to reconstruct the normal paths of these objects. In the experimental results, we demonstrate the efficiency of our approach by showing the reconnection of missing paths under different conditions.