Addressing the lack of visibility and limited coverage for lesser-known sports leagues and women's sports. Gen Z demands shorter, engaging, and easily shareable content.
- Automatically processes video footage using OpenCV and PyTesseract OCR to accurately detect and track scoreboard changes.
- Utilizes NLP (NeMo, NVIDIA) to analyze commentary and rank clips by excitement level.
- Enhances video production efficiency using parallel processing with FFmpeg.
- Real-Time Score Tracking: Precisely extracts scoreboard data from diverse footage.
- Commentary Analysis: Identifies exciting moments using NLP and ranks content for maximum viewer engagement.
- Cost & Time Efficient: Saves up to 14 hours per project by automating the editing process.
- OpenCV
- PyTesseract OCR
- NLP (NeMo, NVIDIA)
- FFmpeg
- FAISS Vector Database
- Incorporate pose detection and object tracking for advanced analytics.
- Expand event recognition to include assists, rebounds, steals, and defensive actions.
- Broaden training dataset for improved accuracy and reduced misclassification.
Special thanks to our hackathon team and mentors who supported this project!