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Temporal-Consistent Visual Clue Attentive Network for Video-Based Person Re-Identification by Bingliang Jiao; Liying Gao; Peng Wang is a scholarly article available to read on EtoBox.

What is Temporal-Consistent Visual Clue Attentive Network for Video-Based Person Re-Identification about?

Video-based person re-identification (ReID) aims to match video trajectories of pedestrians across multi-view cameras and has important applications in criminal investigation and intelligent surveillance. Compared with single image re-identification, the abundant temporal information contained in video sequences makes it describe pedestrian instances more precisely and effectively. Recently, most existing video-based person ReID algorithms have made use of temporal information by fusing diverse visual contents captured in independent frames. However, these algorithms only measure the salience of visual clues in each single frame, inevitably introducing momentary interference caused by factors like occlusion. Therefore, in this work, we introduce a Temporal-consistent Visual Clue Attentive Network (TVCAN), which is designed to capture temporal-consistently salient pedestrian contents among frames. Our TVCAN consists of two major modules, the TCSA module and the TCCA module, which are responsible for capturing and emphasizing consistently salient visual contents from the spatial dimension and channel dimension, respectively. Through extensive experiments, the effectiveness of our des

Author

Bingliang Jiao; Liying Gao; Peng Wang

Publisher

ACM

Published

2022

Language

EN