A mask-guided generation learning loss optimization method for person re-identification

Zebin Yang; Junping Yao; Xiaojun Li; Jiasheng Shi

doi:10.1117/12.3009462

16 October 2023 A mask-guided generation learning loss optimization method for person re-identification

Zebin Yang, Junping Yao, Xiaojun Li, Jiasheng Shi

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031R (2023) https://doi.org/10.1117/12.3009462
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Traditional person re-identification (ReID) methods based on generative adversarial networks often neglect the impact of background clutter during the image generation stage of data augmentation, which can have a negative effect on the accuracy of subsequent recognition results. To address this problem, this paper proposes a mask-guided generation learning loss optimization method, which consists of reconstructed image loss optimization and composite image loss optimization. By optimizing the loss function in the image generation stage, the proposed method generates more real images of the person’s body part to eliminate the impact of background clutter on recognition results in subsequent stages. The reconstructed image loss optimization uses a body mask to construct an attention transfer matrix, mapping the original image into a new feature map containing person-related but background-independent features. The composite image loss optimization uses VGG16 to extract fine-grained features from the composite image and calculates the local loss so that the generation network can further learn the fine-grained features of the body part. Extensive experiments on the Market- 1501 dataset show that the proposed method achieves 94.8% in Rank-1 and 85.5% in mAP, increasing the baseline results by 0.4% and 0.5%, respectively. The proposed method significantly improves the authenticity of the person’s body part in the generated image and the accuracy of ReID, providing high-quality training data for the model to discover more finegrained features. The proposed method provides a new idea for the application of body masks in ReID as well as a feasible technical route for future research.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zebin Yang, Junping Yao, Xiaojun Li, and Jiasheng Shi "A mask-guided generation learning loss optimization method for person re-identification", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031R (16 October 2023); https://doi.org/10.1117/12.3009462

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KEYWORDS

Data modeling

Feature extraction

Image segmentation

Image processing

Image restoration

Education and training

Clutter

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