行人重识别中度量学习方法研究进展
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1.山东理工大学;2.不列颠哥伦比亚大学

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TP391

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A Survey on Metric Learning in Person Re-Identification
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Shandong University of Technology

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    摘要:

    行人重识别是计算机视觉领域极具挑战的研究课题。近年来,伴随大规模行人数据集推出和深度学习发展,针对行人特征提取与描述、距离度量学习两大关键技术的研究取得众多成果。已有综述文献主要对特征提取与描述方法开展了归纳总结,尚缺乏对度量学习方法的全面分析。同时,鉴于度量学习在提升重识别性能中的关键作用,有必要对行人重识别中度量学习研究现状进行系统梳理。本文从距离度量方式、度量学习算法和重排序三方面系统总结了行人重识别度量学习方法,比较了部分典型方法的实验效果,并对未来可能的研究方向做了展望。

    Abstract:

    Person re-identification is a very challenging research topic in the field of computer vision. In recent years, with the emergence of large-scale datasets and development of deep learning, many results have been obtained in the study of person feature extraction and description, distance metric learning. In the existing literature, feature extraction and description have been well summarized, but there is no comprehensive analysis of metric learning. Considering the key role of metric learning in improving person re-identification, it is necessary to systematically review the research status of metric learning in person re-identification. This survey gives a systematic summary of metric learning method from three perspectives: distance metric method, metric learning algorithm and re-ranking algorithm. Then, the performance of some representative methods are compared and analyzed. Finally, we make a prospect for the future research direction of metric learning.

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历史
  • 收稿日期:2020-06-18
  • 最后修改日期:2021-02-24
  • 录用日期:2020-11-05
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