Abstract
Vehicle re-identification is a cross-camera vehicle retrieval method. Compared with the method of manually retrieving surveillance video to realize vehicle re-identification, the method based on deep learning uses computer to achieve cross-camera matching of target vehicles, which saves labor costs, so it has high practical application value in the field of intelligent transportation. Common vehicle re-identification algorithms achieve re-identification by making the characteristics of different pictures of the same id vehicle tend to be consistent. Generally, these methods rely on manually annotated datasets. However, the accuracy of the model may decrease due to the possibility of labeling errors when manually labeling datasets, especially large-scale datasets. To solve the above problems, this paper proposes a vehicle re-identification algorithm based on spatio-temporal multi-instance learning. It uses a multi-instance bag to train a feature extraction model and pays attention to the features of the entire multi-instance bag and ignores the features of a single instance. So it can handle the problem of mislabeling in the dataset. Experimental results show that the model is feasible: on the VeRi dataset, the model can achieve 33.3% mAP and 67.9% Rank-1 accuracy.
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Acknowledgment
This work was supported by Science and Technology Project of Hebei Provincial Department of Transportation under Grant No. RW-202008.
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Yang, X., Li, C., Zeng, Q., Pan, X., Yang, J., Xu, H. (2022). Vehicle Re-identification via Spatio-temporal Multi-instance Learning. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_36
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DOI: https://doi.org/10.1007/978-981-19-6135-9_36
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