Abstract
In recent years, object re-identification (ReID) based on deep learning has made great progress, and research in this field mainly focuses on person and vehicle. However, the researchers ignore an important target: riders. Electric bikes are an essential part of modern transportation scenarios, so identifying riders and monitoring their behavior on a large scale is critical to public safety management. To bridge the research gap of rider ReID, this paper proposes a pyramid attention network (PANet), which utilizes the pyramid structure to capture multi-scale clues and discovers key regional features from fine to coarse. PANet first learns fine-grained attention in local small regions, then gradually aggregates local regions to expand the scope of attention exploration, and finally conducts global coarse-grained attention learning. We implement two different dimensional attention computations in the pyramid attention network: spatial attention and channel attention. Experiments on BPReID and MoRe datasets demonstrate the effectiveness of this network design, which can achieve better performance with limited computational overhead.
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Li, J., Liu, B. (2022). Rider Re-identification Based on Pyramid Attention. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_7
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DOI: https://doi.org/10.1007/978-3-031-18907-4_7
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