STA: Spatial-Temporal Attention for Large-Scale Video-Based Person Re-Identification

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person reidentification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e. g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i. e. MARS and DukeMTMC- VideoReID. In particular, the mAP reaches 87. 7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11. 6%.

Authors

• Yang Fu
• Xiaoyang Wang
• Yunchao Wei Institute of Information Science, Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology
• Thomas Huang

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