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A deep multi-feature distance metric learning method for pedestrian re-identification

  • 1142T: Multi-view Representation Learning and Understanding
  • Published:
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Abstract

Consider the problem that handcrafted features are limited by not being directly applicable to practical problems. Additionally, the deep convolution feature is a high-dimensional feature, and if it is directly used to match the image, it will consume considerable time and memory. Moreover, features from higher levels may be contaminated by dramatic variations in the human pose or background clutter. This paper proposes a method based on deep multi-feature distance metric learning. First, each spatial position and channel are weighted after extracting the deep convolution feature from the last layer of the CNN, and the final aggregation result, that is, the feature of the image, is obtained by sum-pooling. Second, a new method to improve and integrate the convolution feature of the region is proposed. The convolution features are processed by the sliding frame technique, and the low-dimensional eigenvector with dimensions equal to the number of convolution layer channels is obtained. Third, a distance learning algorithm is proposed by cross-view quadratic discriminant analysis metric learning. Finally, the weighted fusion strategy is used to accomplish the collaboration between the handcrafted and deep convolution features. On the Market-1501 and VIPeR datasets, the experimental results show that the rank 1 values of the proposed method on three experimental datasets reach 90.02% and 68.74%, respectively. Under the new classification rules of the CHUK03 dataset, the rank 1 performance of the proposed method reaches 34.2%. The experimental results show that the accuracy of pedestrian re-identification after distance-weighted fusion is higher than that obtained by the separate feature distance metric.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61671376 and Grant 61771386 and in part by the Scientific Research Project of Shaanxi Provincial Department of Education under Grant 18JK0556.

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Authors and Affiliations

  1. College of Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, 5 South Jinhua Road, Xi’an, 710048, Shaanxi Province, China

    Xuan Deng, Kaiyang Liao, Yuanlin Zheng, Guangfeng Lin & Hao Lei

  2. Printing and Packaging Engineering Technology Research Centre of Shaanxi Province, 5 South Jinhua Road, Xi’an, 710048, Shaanxi Province, China

    Kaiyang Liao

  3. Key Lab of Printing and Packaging Engineering of Shaanxi Province, 5 South Jinhua Road, Xi’an, 710048, Shaanxi Province, China

    Yuanlin Zheng

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  1. Xuan Deng
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  2. Kaiyang Liao
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  5. Hao Lei
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Correspondence to Kaiyang Liao.

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Deng, X., Liao, K., Zheng, Y. et al. A deep multi-feature distance metric learning method for pedestrian re-identification. Multimed Tools Appl 80, 23113–23131 (2021). https://doi.org/10.1007/s11042-020-10458-8

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