Pyramid Spatial-Temporal Aggregation for Video-based Person Re-Identification
Yingquan Wang, Pingping Zhang, Shang Gao, Xia Geng, Hu Lu, Dong Wang
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Abstract
Video-based person re-identification aims to associate the video clips of the same person across multiple non-overlapping cameras. Spatial-temporal representations can provide richer and complementary information between frames, which are crucial to distinguish the target person when occlusion occurs. This paper proposes a novel Pyramid Spatial-Temporal Aggregation (PSTA) framework to aggregate the frame-level features progressively and fuse the hierarchical temporal features into a final video-level representation. Thus, short-term and long-term temporal information could be well exploited by different hierarchies. Furthermore, a Spatial-Temporal Aggregation Module (STAM) is proposed to enhance the aggregation capability of PSTA. It mainly consists of two novel attention blocks: Spatial R