Appearance and Motion Enhancement for Video-Based Person Re-Identification

In this paper, we propose an Appearance and Motion Enhancement Model (AMEM) for video-based person reidentification to enrich the two kinds of information contained in the backbone network in a more interpretable way. Concretely, human attribute recognition under the supervision of pseudo labels is exploited in an Appearance Enhancement Module (AEM) to help enrich the appearance and semantic information. A Motion Enhancement Module (MEM) is designed to capture the identity-discriminative walking patterns through predicting future frames. Despite a complex model with several auxiliary modules during training, only the backbone model plus two small branches are kept for similarity evaluation which constitute a simple but effective final model. Extensive experiments conducted on three popular video-based person ReID benchmarks demonstrate the effectiveness of our proposed model and the state-of-the-art performance compared with existing methods.

Authors

• Shuzhao Li
• Huimin Yu Zhejiang University ZJU-League Research & Development Center State Key Lab of CAD&CG, Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking
• Haoji Hu Zhejiang University

Keywords

No keywords are indexed for this paper.