Temporal Regularized Spatial Attention for Video-Based Person Re-Identification

Video-based person re-identification aims at matching video sequences of a person across different camera views. How to explore the abundant appearance and motion information in a video sequence is crucial to tackle this problem. To this end, we first introduce a parameter-free spatial attention module to emphasize the importance of discriminative regions. Then we apply a temporal regularization term on spatial attention to refine corrupted region caused by occlusion and blur. This term allows the attention response at one position in a frame to be related to other frames of the same position. Extensive experiments are conducted on iLIDS-VID and PRID-2011 datasets. The experimental results demonstrate that our approach surpasses the existing state-of-the-art video-based person re-identification methods on iLIDS-VID and PRID-2011.