Deep similarity feature learning for person re-identification

Abstract

Person re-identification aims to match the same pedestrians across different camera views and has been applied to many important applications such as intelligent video surveillance. Due to the spatiotemporal uncertainty and visual ambiguity of pedestrian image pairs, person re-identification remains a difficult and challenging problem. The huge success of deep learning has focused attention on the use of deep features for person re-identification. However, for person re-identification, most deep learning methods minimize cross-entropy or triplet-based losses, thereby neglecting the fact that the similarities and differences between image pairs can be considered simultaneously to increase discrimination. In this paper, we propose a novel deep learning method called deep similarity feature learning (DSFL) to extract more effective deep features for image pairs. Extensive experiments on two representative person re-identification datasets (CUHK-03 and GRID) demonstrate the effectiveness and robustness of DSFL.