2025 SURF项目展示——AI篮球视频分析系统来了!
作者：微信文章AIAC
AI篮球视频分析
系统来了！
2025 SURF项目展示
AIAC
你是否想过，AI能否实时了解比赛中球员的位置和队伍布局？我们的团队开发了一套基于YOLOv11的篮球视频分析系统，可自动处理比赛视频，为教练、解说和球迷提供全新视角。
系统已实现功能
聚焦科技与未来的核心主题，精准锚定科技创新驱动发展的核心逻辑。科技创新是突破发展瓶颈、开拓未来赛道的核心动力。
球员&裁判检测：精准识别场上每位球员和裁判
跟踪：使用DeepSORT追踪，尽量保持身份一致性，重建球员轨迹
队伍分类：通过颜色特征半人工精校自动高精度区分两队球员
骨架估计：集成RTMPose-S，预测球员关键关节点
投影可视化：将球员位置映射到篮球场平面，直观观察队伍布局
实验成果
在真实比赛视频中，系统检测准确率达到98%，队伍分类准确率95%，展示了稳定且高效的分析能力。生成的可视化视频和JSON数据可用于进一步研究或教学示范。
未来预期
自动识别球员动作，如投篮、运球
高级战术分析，辅助教练决策和比赛解说
图1：骨架模型
图2：队伍分类
图3：2D 投影可视化
学生：焦英博、苗一诺、张文婧、苏泓铭、黄皓洋
指导老师：周冕博士
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AI-Powered Basketball Video Analysis!
Ever wondered if AI can understand player positions and team layouts in real time? Our team developed a YOLOv11-based basketball video analysis system, which automatically analyzes game videos, providing a new perspective for coaches, commentators, and fans.
Current Features:
Player & Referee Detection: Accurate identification of all players and referees on the court
Tracking: DeepSORT maintains consistent IDs, reconstructing player trajectories
Team Classification: Automatically and precisely distinguish the two teams of players through color feature semi-artificial fine-tuning.
Pose Estimation: RTMPose-S predicts key body joints for each player
2D Court Projection: Map player positions onto a basketball court plane for intuitive team layout visualisation
Results:
On real game videos, the system achieved 98% detection accuracy and 95% team classification accuracy, demonstrating stable and efficient performance. The visualised videos and structured JSON outputs support further research or teaching applications.
Future Plans:
Automatically recognise player actions such as shooting or dribbling
Advanced tactical analysis to support coaching decisions and game commentary
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