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arXiv · 2108.06946

Video Person Re-identification using Attribute-enhanced Features

Tianrui Chai·Zhiyuan Chen·Annan Li·Jiaxin Chen·Xinyu Mei·Yunhong Wang
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Abstract

Video-based person re-identification (Re-ID) which aims to associate people across non-overlapping cameras using surveillance video is a challenging task. Pedestrian attribute, such as gender, age and clothing characteristics contains rich and supplementary information but is less explored in video person Re-ID. In this work, we propose a novel network architecture named Attribute Salience Assisted Network (ASA-Net) for attribute-assisted video person Re-ID, which achieved considerable improvement to existing works by two methods.First, to learn a better separation of the target from background, we propose to learn the visual attention from middle-level attribute instead of high-level identities. The proposed Attribute Salient Region Enhance (ASRE) module can attend more accurately on the body of pedestrian. Second, we found that many identity-irrelevant but object or subject-relevant factors like the view angle and movement of the target pedestrian can greatly influence the two dimensional appearance of a pedestrian. This problem can be mitigated by investigating both identity-relevant and identity-irrelevant attributes via a novel triplet loss which is referred as the Pose~\&~Motion-Invariant (PMI) triplet loss.

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Tianrui Chai, Zhiyuan Chen, Annan Li, Jiaxin Chen, Xinyu Mei, Yunhong Wang. 2021-08-16. Video Person Re-identification using Attribute-enhanced Features. https://arxiv.org/abs/2108.06946

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