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- W4300603886 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." @default.
- W4300603886 created "2022-10-03" @default.
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- W4300603886 date "2021-08-16" @default.
- W4300603886 modified "2023-10-05" @default.
- W4300603886 title "Video Person Re-identification using Attribute-enhanced Features" @default.
- W4300603886 doi "https://doi.org/10.48550/arxiv.2108.06946" @default.
- W4300603886 hasPublicationYear "2021" @default.
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