Gait recognition based on local relationship learning

Wanzhen Zhou; Junjun Shui; Jianxia Wang

doi:10.1117/12.2684266

1 August 2023 Gait recognition based on local relationship learning

Wanzhen Zhou, Junjun Shui, Jianxia Wang

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127543P (2023) https://doi.org/10.1117/12.2684266
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

To alleviate the influence of angle change and dressing style change on gait recognition, a gait recognition network GaitRL based on local gait relationship learning is proposed. First, the pedestrian gait image is acquired by the camera equipment, the acquired image is preprocessed, and the pedestrian data is aligned with the central axis of the human body. Secondly, the image is segmented and local feature extraction is performed. Third, learn the relationship between local features A_m, calculate the product between Am and the image sequence, and use it as the data feature of the gait. The experimental results show that compared with the GaitSet network, the recognition accuracy of the network is improved by 0.7%, 2.4% and 3% under NM, BG and CL conditions, respectively, which can better mitigate the influence of angle change and dressing style change on gait recognition.

Citation Download Citation

Wanzhen Zhou, Junjun Shui, and Jianxia Wang "Gait recognition based on local relationship learning", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127543P (1 August 2023); https://doi.org/10.1117/12.2684266

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KEYWORDS

Gait analysis

Feature extraction

Education and training

Data modeling

Statistical modeling

Convolution

Matrices

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