NetraPlay
A lightweight, CPU-friendly tool to automatically extract cricket action highlights from long video footage. Uses a hybrid multi-signal approach combining motion detection, pose estimation, and audio analysis for 90% faster editing.
Executive Summary
Extracting 5 minutes of action from 1 hour of cricket net sessions is a massive time sink for coaches and players.
NetraPlay uses a hybrid approach—combining motion detection, pose estimation, and audio analysis—to precisely extract deliveries, reducing editing time by over 90%.
Core Infrastructure
Motion-Based Filtering
Quickly discards dead air and identifies active frames.
Action Recognition
Uses MediaPipe pose estimation to detect batting responses.
Audio-Visual Validation
Cross-references movement with 'ball-on-bat' sound detection.
CPU-Optimized
Runs efficiently on consumer hardware without GPUs.
Design Philosophy
I wanted to build a lightweight, CPU-friendly tool that doesn't require expensive GPUs. The goal was to process video 2-5x faster than real-time on standard hardware.
The 'Hybrid Multi-Signal' breakthrough: Combining Motion Detection, Pose Estimation, and Audio Analysis to confirm ball impact without heavy deep learning.
Technical Architecture
Balancing processing speed with action-detection accuracy. Traditional AI models were too slow for standard CPUs, requiring a custom signal-processing pipeline.
Engineered With
- Python 3.8+
- OpenCV
- MediaPipe
- Librosa
- MoviePy
- NumPy & SciPy
- FFmpeg
Performance Goal
- 2-5x faster than real-time processing
- Low CPU/Memory footprint
- Optimized frame-skipping logic
System Integrity
- Reliable audio-visual signal synchronization
- Robust pose estimation accuracy
- Safe file system operations for exports
