Discriminative dictionary learning sparse coding for person re-identification

Abstract

Person re-identification is one of the most important issues in intelligent transportation systems. Recently, the widespread availability of cameras and a growing need for public safety have increasingly motivated interest in the problem of person re-identification in multi-camera networks. The main difficulty of person re-identification arises from the variations in human pose, different viewpoint in multi-camera, cluttered background, occlusion, and low image resolution, which lead person re-identification to a challenging problem. This paper presents a method based on sparse coding for person re-identification. To apply sparse coding method, we firstly solve the problem of aligning person images, and to enhance the discrimination of dictionary, a dictionary learning model is added into our method. Experiments on benchmark dataset (CAVIARa, ETZH, i-LIDS) demonstrate that the proposed method outperforms the state-of-the-art approaches.