A person re‐identification method for sports event scenes incorporating textual information mining

Author:

Wang Runmin1ORCID,Zhu Yanbin1ORCID,Wan Zukun1,Chen Hua1,Zhu Zhenlin1,Zhou Weixin1,Han Chang2ORCID,Ding Yajun1

Affiliation:

1. The School of Information Science and Engineering Hunan Normal University Changsha Hunan China

2. Laboratory of Control and Application Technology of Robotics Wuhan Business University Wuhan Hubei China

Abstract

AbstractPerson re‐identification represents a pivotal sub‐problem in image retrieval, boasting broad application prospects in fields such as intelligent security and video surveillance. However, most existing person re‐identification methods predominantly focus solely on visual features pertaining to the person targets, thereby disregarding some supporting information closely related to the scene context. In the context of athlete re‐identification during sports event scenes, the athlete bib number is fully considered, an important clue that can provide different athletes' identities, and the traditional visual features of the person and high‐level semantic information of the bib number text are fused. A multi‐source information mutual gain mechanism is designed to improve the accuracy of the person re‐identification task. In the existing only publicly available marathon bib number dataset RBNR, the recognition accuracy of this method is significantly superior to that of the existing person re‐identification method. In addition, this paper constructs and publishes an athlete re‐identification dataset (HNNU‐ReID8000) for mainstream sports events, and the mean average precision (mAP) value of this method reaches 96.1% on this dataset, significantly ahead of existing state‐of‐the‐art person re‐identification methods. The code and the HNNU‐ReID8000 dataset will be released at https://github.com/yanbin‐zhu/zyb_person‐reid.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Publisher

Institution of Engineering and Technology (IET)

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