README.md

metadata

title: AI Sports Coaching
emoji: 🎬
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.34.0
app_file: app.py
pinned: false

🎬 AI Sports Coaching System

A video-based pose estimation and AI analysis platform powered by Vision-Language Models (VLMs). Users can upload videos, perform pose detection, temporal downsampling, and get intelligent feedback.

Features

• 📹 Video Upload: Support for multiple video formats
• 🤖 Pose Estimation: Human keypoint detection
• ⏱️ Temporal Downsampling: Configurable frame rate reduction
• 🧠 AI Analysis: Integrate LLMs/VLMs for intelligent insights
• 🐍 Python Processing: Custom data processing and analysis pipelines
• 📊 Result Visualization: Multi-dimensional result display
• ⭐ Scoring Mechanism: User feedback scoring for outputs

Workflow

1. Video Upload → Upload one or more videos for analysis
2. Pose Estimation → Extract human keypoint data
3. Temporal Downsampling → Reduce frame rate according to settings
4. AI Analysis → Use VLM/LLM to analyze pose features
5. Python Processing → Run custom analysis scripts
6. Result Generation → Produce a comprehensive analysis report
7. Scoring → User rates output quality (e.g., 1–5)

Usage

1. Upload video file(s)
2. Configure downsampling rate (e.g., 1–10)
3. Click “Start Processing”
4. View results in different tabs (pose visualization, analysis report, charts, etc.)
5. Provide feedback score for the outputs

TODO

• Accelerate Pose Estimation

  • Optimize model inference (e.g., model pruning/quantization, GPU/CPU parallelism)
  • Batch processing for multiple videos or frames
  • Investigate lightweight architectures or delegate to hardware accelerators

• Local Deployment of VLMs

  • Documentation for downloading and setting up VLM weights locally
  • Instructions for environment configuration (dependencies, hardware requirements)
  • Offline inference capabilities and fallback strategies
  • Security considerations for storing API keys or model files

• Support Multiple Video Formats

  • Automatic compatibility check and conversion (e.g., mp4, avi, mov, webm)
  • Integrate ffmpeg (or similar) for on-the-fly format handling
  • Graceful fallback or user guidance when format is unsupported

• Extend Scoring & Feedback Loop

  • Store user scores along with video metadata
  • Use scores to fine-tune or adjust analysis parameters over time

• Support Different Language

  • Use different language prompts for different language
  • Update prompts for stable language-control

License

MIT License