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1 June 2023 Multi-label pedestrian attribute recognition model based on domain generalization
Xinming Zhang, Zhenxia Yu, Qiangliang Hu
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Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181U (2023) https://doi.org/10.1117/12.2681718
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China
Abstract
At present, there are distribution differences in the public data set of pedestrian attribute recognition, and the sampling scenarios are diversified, which leads to the reduction of the prediction accuracy of the pedestrian attribute recognition algorithm (PAR) in the cross-data set and the limitation of its application in the actual scene. For the appeal problem, this paper proposes a multi-source and multi-label pedestrian attribute recognition method based on domain generalization (DG). First, label multiple data sets as source domains through label alignment to determine the common attribute labels in each source domain. Secondly, add the countermeasure data enhancement module (ADM) to generate the data distribution of the target domain based on the difference of data distribution in the source domain. This model can predict the data in the unknown target domain, thus optimizing the performance of pedestrian attribute recognition (PAR) in cross-domain and actual scenarios. The model was tested on the public data sets PETA, RAP, PA-100K. Compared with HPNet, LGNet, PGDM, StrongBaseline and other models, the average recognition accuracy (mA) index and F1 value increased by 1.16% and 1.51% respectively, which proved the effectiveness of the model in pedestrian attribute recognition.
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Xinming Zhang, Zhenxia Yu, and Qiangliang Hu "Multi-label pedestrian attribute recognition model based on domain generalization", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181U (1 June 2023); https://doi.org/10.1117/12.2681718
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KEYWORDS
Education and training
Detection and tracking algorithms
Machine learning
Target recognition
Deep learning
Mathematical optimization
Neural networks
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