Eliminating domain deviation via synthetic data for vehicle re-identification

Zhipeng Gao; Tingting Wu; Leijie Lin; Jianqiang Zhao; Anguo Zhang; Junyi Wu

doi:10.1117/12.2640863

2 December 2022 Eliminating domain deviation via synthetic data for vehicle re-identification

Zhipeng Gao, Tingting Wu, Leijie Lin, Jianqiang Zhao, Anguo Zhang, Junyi Wu

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 1228803 (2022) https://doi.org/10.1117/12.2640863
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

Vehicle re-identification (Re-ID) methods with supervised learning achieve high accuracy, but rely heavily on effective supervised labels, so that it cannot extend them to the unsupervised domain. Since the vehicle presents great changes from different perspectives and the large distribution gap between different datasets. Therefore, how to design a vehicle Re-ID method on label-free datasets and show outstanding performance is a difficult problem. In this paper, we propose an unsupervised vehicle Re-ID framework based on synthetic data. Our proposed framework consists of three steps: (1)we use synthetic data to generate pseudo-target samples similar in style to the target domain and use them for model pre-train; (2)the pre-train model is fine-tuned by the source and target domain to improve the cross-domain generalization of the model; (3)the orientation and the camera similarity are calculated by the pre-train orientation and the camera model of the synthetic data, thus punishing the final similarity. Experiments show that the proposed method outperforms existing stateof-the-art methods on benchmark datasets.

Citation Download Citation

Zhipeng Gao, Tingting Wu, Leijie Lin, Jianqiang Zhao, Anguo Zhang, and Junyi Wu "Eliminating domain deviation via synthetic data for vehicle re-identification", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 1228803 (2 December 2022); https://doi.org/10.1117/12.2640863

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