Detection and Tracking of Moving Basketball in Video Streams

Zhang Weixin; Zhong Yingchun; Zhang Gang; Zhong Ling; Lai Zhifei; Yang Kaikang

doi:10.12052/gdutxb.240037

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Detection and Tracking of Moving Basketball in Video Streams

更新时间：2026-05-27

- Detection and Tracking of Moving Basketball in Video Streams
- Vol. 42, Issue 3, Pages: 62-71(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信息工程学院,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.240037
  CLC： TP391.4
- Published：2025
- 稿件说明：
移动端阅览
张炜昕, 钟映春, 张钢, 等. 视频流中移动篮球的检测与跟踪方法[J]. 广东工业大学学报, 2025, 42(3): 62–71.

Zhang Weixin, Zhong Yingchun, Zhang Gang, et al. Detection and Tracking of Moving Basketball in Video Streams[J]. 2025, 42(3): 62-71.
张炜昕, 钟映春, 张钢, 等. 视频流中移动篮球的检测与跟踪方法[J]. 广东工业大学学报, 2025, 42(3): 62–71. DOI： 10.12052/gdutxb.240037

Zhang Weixin, Zhong Yingchun, Zhang Gang, et al. Detection and Tracking of Moving Basketball in Video Streams[J]. 2025, 42(3): 62-71. DOI： 10.12052/gdutxb.240037.

摘要

检测与跟踪视频中的篮球可为教练复盘比赛提供关键信息。在比赛的视频流中，由于篮球目标较小，YOLOv5(You Only Look Once v5)算法对篮球与其他类圆形小目标的区分度较低。为此，在YOLOv5算法的基础上，首先采用V-C3(VoVNet C3)模块代替原C3模块，以解决篮球特征单一的问题，并通过K-L散度(Kullback-Leibler Divergence)验证改进的有效性。其次，采用桥式路径聚合网络(Bridge Path Aggregation Network

BPANet)代替原路径聚合网络(Path Aggregation Network

PANet)，以解决场景中小目标篮球的检测问题。第三，构建分类惩罚机制，以降低篮球与相似目标的误检率。第四，探讨了各参数对篮球检测算法性能的影响，并探寻最佳参数取值和模型结构。实验结果表明，改进后算法的识别精度比原始YOLOv5算法提高了3个百分点，在COCO部分数据集上平均精度提高了2.4个百分点，算法的参数规模降低了5.3个百分点。本文对YOLOv5算法提出的4种改进策略，在保持较高实时性的基础上提高了视频中篮球目标的检测精度并降低模型规模，为类似的目标检测提供了一种新思路。

Abstract

Detecting and tracking basketball in videos are helpful for coaches to review gameplays. In video streams of games

the You Only Look Once v5 (YOLOv5) algorithm exhibits low discriminative ability between basketball and other small circular targets due to the small size of the basketball target. To address this issue

we propose several improvements based on the YOLOv5. Firstly

we replace the original C3 module with the VoVNet C3 (V-C3) module to address the problem of limited basketball features and validate the effectiveness of this enhancement through Kullback-Leibler divergence. Secondly

we introduce the Bridge Path Aggregation Network (BPANet) to replace the Path Aggregation Network (PANet) for better detection of small basketball targets in the scene. Thirdly

a classification penalty mechanism is constructed to reduce false alarms between basketball and similar targets. Lastly

we explore the influence of various parameters on the performance of the basketball detection algorithm to determine optimal parameter values and model structures. Experimental results demonstrate that the improved algorithm improves recognition accuracy by approximately 3% over the original YOLOv5 algorithm

with an average precision increase of about 2.4% on the COCO dataset

and reduces the algorithm's parameter size by about 5.3%. The proposed four enhancement strategies of this study based on the YOLOv5 algorithm improve the detection accuracy of basketball targets in videos while reducing model complexity

thereby offering a new approach for similar object detection tasks.

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references

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