Tracking System With Re-Identification Using A Graph Kernels Approach

This paper addresses people re-identification problem for visual surveillance applications. Our approach is based on a rich description of each occurrence of a person thanks to a graph encoding of its salient points. People appearance in a video is encoded by bags of graphs whose similarities are encoded by a graph kernel. Such similarities combined with a tracking system allow us to distinguish a new person from a re-entering one into a video. The efficiency of our method is demonstrated through experiments.