VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification

Alsehaim, A.; Breckon, T.P.

Abstract

Video-based person Re-identification (Re-ID) has received increasing attention recently due to its important role within surveillance video analysis. Video-based Re- ID expands upon earlier image-based methods by extracting person features temporally across multiple video image frames. The key challenge within person Re-ID is extracting a robust feature representation that is invariant to the challenges of pose and illumination variation across multiple camera viewpoints. Whilst most contemporary methods use a CNN based methodology, recent advances in vision transformer (ViT) architectures boost fine-grained feature discrimination via the use of both multi-head attention without any loss of feature robustness. To specifically enable ViT architectures to effectively address the challenges of video person Re-ID, we propose two novel modules constructs, Temporal Clip Shift and Shuffled (TCSS) and Video Patch Part Feature (VPPF), that boost the robustness of the resultant Re-ID feature representation. Furthermore, we combine our proposed approach with current best practices spanning both image and video based Re-ID including camera view embedding. Our proposed approach outperforms existing state-of-the-art work on the MARS, PRID2011, and iLIDS-VID Re-ID benchmark datasets achieving 96.36%, 96.63%, 94.67% rank-1 accuracy respectively and achieving 90.25% mAP on MARS.

Citation

Alsehaim, A., & Breckon, T. (2022, November). VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification. Presented at BMVC 2022: The 33rd British Machine Vision Conference, London, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: BMVC 2022: The 33rd British Machine Vision Conference
• Start Date: Nov 21, 2022
• End Date: Nov 24, 2022
• Acceptance Date: Sep 30, 2022
• Online Publication Date: Nov 21, 2022
• Publication Date: 2022-11
• Deposit Date: Oct 13, 2022
• Publicly Available Date: Nov 24, 2022
• Public URL: https://durham-repository.worktribe.com/output/1134965
• Publisher URL: https://britishmachinevisionassociation.github.io/bmvc