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Attribute-aware Identity-hard Triplet Loss for Video-based Person Re-identification

2020-06-13 09:15:38
Zhiyuan Chen, Annan Li, Shilu Jiang, Yunhong Wang
arXiv_CV
         
Abstract

Video-based person re-identification (Re-ID) is an important computer vision task. The batch-hard triplet loss frequently used in video-based person Re-ID suffers from the Distance Variance among Different Positives (DVDP) problem. In this paper, we address this issue by introducing a new metric learning method called Attribute-aware Identity-hard Triplet Loss (AITL), which reduces the intra-class variation among positive samples via calculating attribute distance. To achieve a complete model of video-based person Re-ID, a multi-task framework with Attribute-driven Spatio-Temporal Attention (ASTA) mechanism is also proposed. Extensive experiments on MARS and DukeMTMC-VID datasets shows that both the AITL and ASTA are very effective. Enhanced by them, even a simple light-weighted video-based person Re-ID baseline can outperform existing state-of-the-art approaches. The codes has been published on this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2006.07597

PDF

https://arxiv.org/pdf/2006.07597.pdf

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