A Research on Consistent Tracking of Athlete Identities in Basketball Game Video Streams

Liu Jiajie; Zhong Yingchun; Zhang Gang; Lai Zhifei; Yang Kaikang; Zhang Yongheng

doi:10.12052/gdutxb.240072

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A Research on Consistent Tracking of Athlete Identities in Basketball Game Video Streams

更新时间：2026-05-27

- A Research on Consistent Tracking of Athlete Identities in Basketball Game Video Streams
- Vol. 42, Issue 4, Pages: 48-58(2025)
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 广州番禺职业技术学院,广东,广州,511483
  3. 深圳云动家体育科技有限公司,广东,深圳,518115
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.240072
  CLC： TP391.41
- Published：2025
- 稿件说明：
移动端阅览
刘嘉杰, 钟映春, 张钢, 等. 篮球比赛视频流中运动员身份的一致性跟踪研究[J]. 广东工业大学学报, 2025, 42(4): 48–58.

Liu Jiajie, Zhong Yingchun, Zhang Gang, et al. A Research on Consistent Tracking of Athlete Identities in Basketball Game Video Streams[J]. 2025, 42(4): 48-58.
刘嘉杰, 钟映春, 张钢, 等. 篮球比赛视频流中运动员身份的一致性跟踪研究[J]. 广东工业大学学报, 2025, 42(4): 48–58. DOI： 10.12052/gdutxb.240072

Liu Jiajie, Zhong Yingchun, Zhang Gang, et al. A Research on Consistent Tracking of Athlete Identities in Basketball Game Video Streams[J]. 2025, 42(4): 48-58. DOI： 10.12052/gdutxb.240072.

摘要

在篮球比赛视频流中，保持每个快速移动的运动员始终具有一致性的身份是重建运动员移动轨迹的关键环节之一。当前的主流算法在跟踪运动员时，普遍存在身份一致性跟踪精度较低的问题。为此，本文提出了3种改进策略：首先使用Soft-NMS (Soft-non-maximum Suppression) 和Focal EIoU (Efficient IoU) Loss代替原有的NMS (Non-maximum Suppression) 和损失函数，以提高运动员在被遮挡情况下的检测精度；其次提出了一种遮挡轨迹匹配机制，以解决运动员相互遮挡时的身份易混淆问题；最后增加了提取运动员号码牌数字特征的模块，并利用该特征匹配链接运动员在离开相机视野前和再次进入相机视野后的移动轨迹。在主流数据集上的实验结果表明，本文算法的HOTA (Higher Order Tracking Accuracy) 指标比原始DeepSORT (Deep Simple Online and Realtime Tracking) 算法分别提高了11.21和6.63个百分点，IDF1(IDF1 Score) 指标提高了15.56和10.11个百分点，身份变化次数(ID Switch

IDSW) 分别减少了36%和27%；与当前性能较优的OCSORT (Observation-Centric Simple Online and Realtime Tracking) 算法相比，本文算法的HOTA指标分别提高了2.39和0.98个百分点，MOTA (Multi-Object Tracking Accuracy) 指标分别提高了7.48和7.92个百分点。由此可见，本文提出的改进方法能够提高视频流中篮球运动员身份在全场比赛中的一致性，较有效地解决身份变化与混淆等问题，为重建运动员在全场比赛的移动轨迹奠定了基础。

Abstract

The accuracy issues of player tracking in basketball game video streams due to occlusions and identity loss

which are crucial for reconstructing players' movement trajectories throughout the game

are addressed. To overcome the limitations of the DeepSORT algorithm in handling occlusions and maintaining identity consistency

three improvements are proposed. Firstly

the integration of Soft-NMS and Focal E-IoU loss functions enhances detection performance during occlusions. Secondly

an occlusion trajectory matching mechanism is introduced to reduce identity confusion when players occlude each other. Finally

a module for extracting and linking number plate features on players' jerseys is added to resolve identity changes when players re-enter the field of view. On main popular datasets

the proposed algorithm demonstrates significant improvements over the original DeepSORT with an 11.21 percentage points and 6.63 percentage points increase in the HOTA metric

a 15.56 percentage points and 10.11 percentage points improvement in the IDF1 metric

and a reduction of 36% and 27% in the number of identity changes. Compared with the competitive OCSORT algorithm

this method further enhances the HOTA metric by 2.39 percentage points and 0.98 percentage points

and the MOTA metric by 7.48 percentage points and 7.92 percentage points. The enhancements effectively improve the tracking precision of basketball players in video streams

particularly in dealing with challenges such as identity switches and occlusions

thereby laying a solid foundation for accurately reconstructing the full-game movement trajectories of athletes.

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Keywords

references

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