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

Aishah Alsehaim (Durham university),* Toby P Breckon (Durham University)
The 33rd British Machine Vision Conference


Video-based person Re-identification (Re-ID) is receiving increasing attention recently due to its important role within surveillance video analysis. Video-based ReID 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 address effectively the challenges of video person Re-ID, here 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 for MARS.



author    = {Aishah Alsehaim and Toby P Breckon},
title     = {VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {}

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