Pose-attention: a novel baseline for person re-identification

Zhijun He; Hongbo Zhao; Na Yi; Wenquan Feng

doi:10.1117/12.2604694

5 October 2021 Pose-attention: a novel baseline for person re-identification

Zhijun He, Hongbo Zhao, Na Yi, Wenquan Feng

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 1191118 (2021) https://doi.org/10.1117/12.2604694
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

This paper proposes a novel baseline for deep person ReID methods by introducing human pose-based attention mechanism. Benefiting from deep convolutional network, there has been great progress of person re-identification (ReID) in recent years, which aims at retrieving the same person identities from images captured by different cameras. Most of existing methods focus on designing complex network structures to achieve higher scores on public datasets, but few works pay attention to baseline design. A strong baseline is crucial in experiments and could make the elaborated proposed methods more convincing. The present study makes use of a pre-trained human pose estimator to extract human key-point information. Then, we propose a novel manner to fuse pose information with global feature from Resnet50, which could lead the network concentrate more on discriminative key-point feature areas. Our work could achieve 94.8% rank-1 accuracy & 87.4% mean average precision (mAP) on Market1501, and outperform all other existing baselines that only use Resnet50 to our best knowledge. What’s more, experiment results also suggest that with the help of pose information, our work could naturally be robust against misalignment and occlusion problems.

Citation Download Citation

Zhijun He, Hongbo Zhao, Na Yi, and Wenquan Feng "Pose-attention: a novel baseline for person re-identification", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 1191118 (5 October 2021); https://doi.org/10.1117/12.2604694

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KEYWORDS

Atomic force microscopy

Feature extraction

Cameras

Network architectures

Image fusion

Structural design

Image resolution

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