Learning discriminative local contexts for person re-identification in vehicle surveillance scenarios

In recent years, person re-identification (Re-ID) has been widely used in intelligent surveillance and security. However, Re-ID faces many challenges in the vehicle surveillance scenario, such as heavy occlusion, misalignment, and similar appearances. Most Re-ID methods focus on learning discriminative global features or dividing regions for local feature learning, which may ignore critical but subtle differences between pedestrians. In this paper, we propose a local context aggregation branch for learning discriminative local contexts at multiple scales, which can supplement the critical detailed information omitted in global features. Specifically, we exploit dilated convolutions to simulate spatial feature pyramid to capture multi-scale spatial contexts efficiently. The essential information that can distinguish different pedestrians is then emphasized. Besides, we construct a Re-ID dataset named BSV for vehicle surveillance scenarios and propose a triplet loss with station constraint enhancement, which utilizes additional valuable station information to construct penalty terms to improve the performance of Re-ID further. Extensive experiments are conducted on the proposed BSV dataset and two standard Re-ID datasets, and the results validate the effectiveness of our method.