Robust color invariant model for person re-identification

Abstract

Person re-identification in a surveillance video is a challenging task because of wide variations in illumination, viewpoint, pose, and occlusion. In this paper, from feature representation and metric learning perspectives, we design a robust color invariant model for person re-identification. Firstly, we propose a novel feature representation called Color Invariant Feature (CIF), it is robust to illumination and viewpoint changes. Secondly, to learn a more discriminant metric for matching persons, XQDA metric learning algorithm is improved by adding a clustering step before computing metric, the new metric learning method is called Multiple Cross-view Quadratic Discriminant Analysis (MXQDA). Experiments on two challenging person re-identification datasets, VIPeR and CUHK1, show that our proposed approach outperforms the state of the art.