Pose guided adaptive graph convolutional network based on pose for  obscured pedestrian re-identification

Run Liu; Shujuan Wang

doi:10.1117/12.2646845

7 September 2022 Pose guided adaptive graph convolutional network based on pose for obscured pedestrian re-identification

Run Liu, Shujuan Wang

Author Affiliations +

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290A (2022) https://doi.org/10.1117/12.2646845
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

In the Reid method which is based on deep learning, the combination of local features and global features can provide a robust representation for person retrieval. Human pose information can provide the location of the human skeleton, thus effectively directing the network to focus more on these critical regions, while also helping to reduce noise interference from background or occlusion. However, previous pose based approaches may not take full advantage of the pose information and rarely consider the different contributions of independent local features. In this paper, we design a pose guided multi-branch graph convolutional attention network (PG-MBGCAN) for the problem of re-identification of occluded pedestrians, which exploits the pose relationship to improve the robustness of pedestrian features and considers the contributions of different local features. Experimental results show that the proposed method in this paper has good performance in different datasets.

Citation Download Citation

Run Liu and Shujuan Wang "Pose guided adaptive graph convolutional network based on pose for obscured pedestrian re-identification", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290A (7 September 2022); https://doi.org/10.1117/12.2646845

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KEYWORDS

Mining

Feature extraction

Convolution

Cameras

Network architectures

Computer programming

Data modeling

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