视频流中移动篮球的检测与跟踪方法

张炜昕; 钟映春; 张钢; 钟灵; 赖志飞; 杨铠康

doi:10.12052/gdutxb.240037

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

视频流中移动篮球的检测与跟踪方法

计算机科学与技术 | 更新时间：2026-05-27

- 视频流中移动篮球的检测与跟踪方法
- Detection and Tracking of Moving Basketball in Video Streams
- 广东工业大学学报 2025年42卷第3期页码：62-71
- 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：
  
  张炜昕(2001–)，男，硕士研究生，主要研究方向为计算机视觉、图像处理，E-mail：1157634890@qq.com
  钟映春(1973–)，男，副教授，博士，主要研究方向为模式识别与图像理解，E-mail：gzzhw@126.com
- 基金信息：
  
  国家自然科学基金资助项目(61975248)
- DOI：10.12052/gdutxb.240037
  中图分类号： TP391.4
- 纸质出版：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.

关键词

Keywords

references

LIU Y F, CHEN R, ZHANG M X, et al . Path for basketball development with Chinese characteristics: connotation, dilemma and future[J ] . Journal of Shanghai University of Sport, 2023, 47(7): 53-63.

陈腾. 面向篮球运动的智能比赛解析系统设计与实现[D]. 北京: 北京邮电大学, 2021.

张芳婷. 基于改进YOLOv5算法的篮球机器人目标检测系统研究[D]. 重庆: 重庆大学, 2022.

D’ORAZIO T, GUARAGNELLA C, LEO M, et al . A new algorithm for ball recognition using circle Hough transform and neural classifier[C ] //2003 Pattern Recognition Society. Kidlington: Elsevier Ltd., 2004: 393-408.

LEO M, D’ORAZIO T, SPAGNOLO P, et al . SIFT based ball recognition in soccer images[C ] //3rd International Conference on Image and Signal Processing. Berlin: Springer, 2008: 263-272.

VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]//2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Los Alamitos, CA: IEEE, 2001: 511-518.

DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Los Alamitos, CA: IEEE, 2005: 886-893.

LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

GIRSHICK R, DONAHUE J, DARRELL T, et al . Rich feature hierarchies for accurate object detection and semantic segmentation[C ] //2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2014: 580-587.

GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). Piscataway: IEEE Press, 2015: 1440-1448.

REN S, HE K, GIRSHICK R, et al . Faster R-CNN: towards real-time object detection with region proposal networks[C ] //Advances in Neural Information Processing Systems 28 (NIPS). La Jolla: NIPS, 2015: 91-99.

LIANG Q K, MEI L, WU W N, et al . Automatic basketball detection in sport video based on R-FCN and Soft-NMS[C ] //2019 4th International Conference on Automation, Control and Robotics Engineering. New York: Association for Computing Machinery, 2019: 1-6.

DAI J F, HE K M, SUN J. R-FCN: object detection via region-based fully convolutional networks[C]//30th International Conference on Neural Information Processing Systems. Morehouse: Curran Associates Inc, 2016: 379-387.

LIU W, et al . SSD: single shot multibox detector[C ] //2016 European Conference on Computer Vision. Cham: Springer, 2016: 21-37.

REDMON J, DIVVALA S, GIRSHICK R, et al . You only look once: unified, real-time object detection[C ] //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 779-788.

SUSANTO E, RUDIAWAN R, ANALIA P, et al . The deep learning development for real-time ball and goal detection of barelang-FC[C ] //2017 International Electronics Symposium on Engineering Technology and Applications (IES-ETA). Piscataway: IEEE Press, 2017: 146-151.

CHEN C, CHEN W, ZHOU S. Object detection of basketball robot based on MobileNet-SSD[C]//Intelligent Equipment, Robots, and Vehicles. Singapore: Springer, 2021: 11-22.

HIEMANN A, KAUTZ T, ZOTTMANN T, et al . Enhancement of speed and accuracy trade-off for sports ball detection in videos—finding fast moving, small objects in real time[J ] . Sensors, 2021, 21(9): 3214.

王景鑫, 潘欣. 一种基于Labelimg的辅助标注方法[J]. 科技创新与应用, 2023, 13(29): 145-148.

SHAO Y H, ZHANG D, CHU H Y, et al . A review of YOLO object detection based on deep learning[J ] . Journal of Electronics Information Technology, 2022, 44(10): 3697-3708.

LEE Y W, HWANG J W, LEE S, et al . An energy and gpu-computation efficient backbone network for real-time object detection[C ] //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2019). Piscataway: IEEE Press, 2019: 752-760.

HE K M, ZHANG X Y, REN S Q, et al . Deep residual learning for image recognition[C ] //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2016: 770-778.

LIN T Y, GOYAL P, GIRSHICK R. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 42(2): 318-327.

HU S M, ZHAO F, LU H Z, et al . Improving YOLOv7-tiny for infrared and visible light image object detection on drones[J ] . Remote Sensing., 2023, 15(13): 3214.

HUANG G, LIU Z, VAN D M L, et al . Densely connected convolutional networks[C ] //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2017: 4700-4708.

HAO S, ZHAO X S, MA X, et al . Multi-class defect target detection method for transmission lines based on TR-YOLOv5[J ] . Journal of Graphics, 2023, 44(4): 667-676.

ZHANG M, YIN L J. Solar cell surface defect detection based on improved YOLO v5[J]. IEEE Access, 2022, 10: 80804-80815.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于PSI技术的福州市地面形变监测与影响因素分析

具守恒量波动方程的循环双子网物理信息神经网络

多尺度对比嵌入增强的自适应入侵检测模型

多套餐共享储能模式下考虑功率互济的社区联盟优化运行策略

面向灾后救援的多无人机通感算一体化资源分配优化方法