Implementation of paper - TrackNetV3: Enhancing ShuttleCock Tracking with Augmentations and Trajectory Rectification
TrackNetV3 : Beyond TrackNetV2 ,and "First" TrackNet using Attention
🔦 Pytorch implementation of TrackNet
Volleyball tracking - VballNet is a specialized deep learning framework designed for volleyball tracking, built upon the foundation of TrackNetV4. This repository includes two primary models, VballNetV1 and VballNetFastV1
Shuttlecock hitting event detection and rally analysis in badminton using computer vision (TrackNet/YOLO) and machine learning (F0/F1 feature sets) for match winner prediction.
🏸 Analyze badminton performance in real-time with AI-driven player tracking, shot analysis, and interactive dashboards for insightful training.
Single-camera tennis analysis pipeline using YOLO and TrackNet for court, player, and ball detection with geometric reconstruction.
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
Scalable Flask API powering the Badminton AI Analyser. Orchestrates a concurrent multi-model pipeline (YOLO, StrongSORT, TrackNet, SlowFast) on Google Cloud Platform to process high-speed match footage.
python computer-vision opencv machine-learning deep-learning yolov3 sort tracknet object-tracking ball-tracking sports-analytics video-analytics real-time tennis
🖥️ Unify multiple GPUs into a single logical accelerator to boost compute efficiency and simplify multi-GPU execution and scheduling.
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