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• W4312622080 abstract "Person Re-identification (Re-ID) plays an important role in the search for missing people and the tracking of suspects. Person Re-identification based on deep learning has made great progress in recent years, and the application of pedestrian contour map has also been concerned. In the study, we found that the pedestrian contour feature is not enough in the shallow expression of CNN. On this basis, in order to improve the recognition performance of Re-ID network, we propose a multi-scale pedestrian contour embedding method. The proposed method uses ResNet50 as the backbone to extract the global features of people. We use the pedestrian contour map as a feature map and reduce its dimension. The pedestrian contour is added to the previous layers of CNN network as a supplement to feature map extraction. In addition, we also apply our method to the local convolution baseline (AlignedReID++). The experimental results show that our method improves the mAP and Rank-1 of the baseline model by 4.5% and 3.6% respectively on Market1501. The mAP and Rank-1 of the local baseline model AlignedReID++ are increased by 0.8% and 1.4%. The final result of our experiment achieves 88.3% mAP and 92.3% Rank-1 accuracy for Market1501, 81.7% mAP and 86.5% Rank-1 accuracy for DukeMTMC-reID." @default.
• W4312622080 created "2023-01-05" @default.
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• W4312622080 date "2022-01-01" @default.
• W4312622080 modified "2023-10-16" @default.
• W4312622080 title "Person Re-identification Based on CNN with Multi-scale Contour Embedding" @default.
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• W4312622080 doi "https://doi.org/10.1007/978-3-031-20497-5_46" @default.
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