A unified hierarchical appearance model for people re-identification using multi-view vision sensors

Abstract

Surveillance of wide areas requires a system of multiple cameras to keep observing people. In such a multiple view system, the people appearance obtained in one camera is usually different from the ones obtained in other cameras. In order to correctly identify people, the unique appearance model of each specific object should be invariant to such changes. In this paper, our appearance model is represented by a hierarchical structure where each node maintains a color Gaussian mixture model (GMM). The re-identification is performed with Bayesian decision. Experimental results show our unified appearance model is robust to rotation and scaling variations. Furthermore, it achieves high accuracy rate (92.7% in average) and high processing performance (above 30 FPS) without tracking mechanism. © 2008 Springer.