UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Semantic-Aware Occlusion-Robust Network for Occluded Person Re-Identification

Zhang, X; Yan, Y; Xue, J-H; Hua, Y; Wang, H; (2020) Semantic-Aware Occlusion-Robust Network for Occluded Person Re-Identification. IEEE Transactions on Circuits and Systems for Video Technology 10.1109/tcsvt.2020.3033165. (In press). Green open access

[thumbnail of TCSVT-XiaokangZhang-accepted.pdf]
Preview
Text
TCSVT-XiaokangZhang-accepted.pdf - Accepted Version

Download (1MB) | Preview

Abstract

In recent years, deep learning-based person re-identification (Re-ID) methods have made significant progress. However, the performance of these methods substantially decreases when dealing with occlusion, which is ubiquitous in realistic scenarios. In this paper, we propose a novel semantic-aware occlusion-robust network (SORN) that effectively exploits the intrinsic relationship between the tasks of person Re-ID and semantic segmentation for occluded person Re-ID. Specifically, the SORN is composed of three branches, including a local branch, a global branch, and a semantic branch. In particular, the local branch extracts part-based local features, and the global branch leverages a novel spatial-patch contrastive loss (SPC) to extract occlusion-robust global features. Meanwhile, the semantic branch generates a foreground-background mask for a pedestrian image, which indicates the non-occluded areas of the human body. The three branches are jointly trained in a unified multi-task learning network. Finally, pedestrian matching is performed based on the local features extracted from the non-occluded areas and the global features extracted from the whole pedestrian image. Extensive experimental results on a large-scale occluded person Re-ID dataset (i.e., Occluded-DukeMTMC) and two partial person Re-ID datasets (i.e., Partial-REID and Partial-iLIDS) show the superiority of the proposed method compared with several state-of-the-art methods for occluded and partial person Re-ID. We also demonstrate the effectiveness of the proposed method on two general person Re-ID datasets (i.e., Market-1501 and DukeMTMC-reID).

Type: Article
Title: Semantic-Aware Occlusion-Robust Network for Occluded Person Re-Identification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/tcsvt.2020.3033165
Publisher version: https://doi.org/10.1109/tcsvt.2020.3033165
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Feature extraction, Semantics, Task analysis, Pose estimation, Image segmentation, Clutter, Cameras
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10116297
Downloads since deposit
482Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item