Abstract
Bus passenger re-identification is a special case of person re-identification, which aims to establish identity correspondence between the front door camera and the back door camera. In bus environment,it is hard to capture the full body of the passengers. So this paper proposes a bus passenger re-identification dataset,which contains 97,136 head images of 1,720 passengers obtained from hundreds of thousands of video frames with different lighting and perspectives. We also provide a evaluation applied to the dataset based on deep learning and triplet loss. After data augmentation,using ResNet with trihard loss as benchmark network and pre-training on pedestrian re-identification dataset Market-1501, we achieve mAP accuracy of 55.79% and Rank-1 accuracy of 67.91% on passenger re-identification dataset.
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Funding
This research has been supported by National Natural Science Foundation of China (U1509207, 61572357and 61872270). Natural Science Foundation of Tianjin (18JCYBJC85500), Tianjin Science and Technology Project(18ZXZNGX00150).
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Guo, J., Xue, Y., Cai, J. et al. A bus passenger re-identification dataset and a deep learning baseline using triplet embedding. Multimed Tools Appl 80, 16425–16440 (2021). https://doi.org/10.1007/s11042-020-08944-0
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DOI: https://doi.org/10.1007/s11042-020-08944-0