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

2020; Association for the Advancement of Artificial Intelligence; Volume: 34; Issue: 07 Linguagem: Inglês

10.1609/aaai.v34i07.6802

2374-3468

Shuzhao Li, Huimin Yu, Haoji Hu,

Gait Recognition and Analysis

In this paper, we propose an Appearance and Motion Enhancement Model (AMEM) for video-based person re-identification 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.