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羽毛球比赛视频智能分析 | Badminton match video analytics: TrackNet shuttle detection + YOLOv8s-pose player tracking + court homography + bullet-time FX. Runs end-to-end on Apple Silicon. 完整文档与 AI agent 任务包见 HANDOVER.md
End-to-end football computer-vision analytics: detection, tracking, dynamic teams & roles, pitch calibration, speed/distance, tactics, possession & an AI match report. YOLO11 + BoT-SORT.
Robust Player Tracking & Behavior Analysis pipeline for SoccerNet Benchmark. Features YOLO11x, BoT-SORT (GMC), and Adaptive Field Masking. Artificial Vision Project 2025/26 @ Unisa.
Soccerlytics is an comprehensive football video analysis system that tracks and analyzes matches. It generates enhanced visualizations with player tracking, ball possession statistics, team assignments, and a unique 2D top-down view for tactical analysis.
Automated football match event detection from broadcast video — YOLOv8 player/ball detection, real-time tracking, interactive player tagging, replay filtering, and structured event output for sports analytics workflows.
Winner, NFL x UCL CDI x AWS Big Data Bowl Hackathon (London). Uses NFL player-tracking data to measure whether QBs gain or lose when they improvise under pressure.
Football player detection, tracking, and re-identification from video using YOLOv11, IoU-based motion matching, and appearance-based ReID with HSV feature descriptors.
Deep learning pipeline for player detection and analytics using YOLO, DeepSORT, and custom metrics covers model training, tracking, feature extraction, insights, and visualization for sports data.
Football match video analysis using computer vision. Features YOLO object detection, ByteTrack player tracking, unsupervised team classification, and 2D pitch minimap generation.
Real-time soccer player tracking & re-identification with YOLOv11, plus a benchmarked comparison of ByteTrack, BoT-SORT, DeepSORT, and Norfair on 720p/4K footage. Python · OpenCV · Ultralytics.