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Overview
This project creates an immersive virtual reality ice-hockey game using advanced computer vision techniques. Real-time analysis of video streams detects players, pucks, and the game arena. Pose estimation captures natural player movements, while OCR extracts player details for realism. The result is an engaging VR experience where players can authentically interact with ice-hockey action.
Challenges
Real-time Video Analysis:
- Detecting players, pucks, and the game arena in real-time requires efficient processing to avoid performance issues and delays.
Natural Player Movement:
- Accurately capturing authentic player movements from video frames while accounting for camera angles and obstructions is a challenge.
Accurate 3D Realism:
- Converting 2D player poses to precise 3D coordinates without sacrificing spatial accuracy is essential for a realistic VR experience.
Solution
Data Annotation Expertise:
- A skilled team annotates 2D video frames to ensure top-quality training data, enhancing CNN-based pose prediction.
Optimized Libraries:
- Utilizing OpenCV and the JDE Tracker improves object detection and tracking speed, resulting in smoother VR gameplay.
CNN-powered Pose Estimation:
- CNNs trained on annotated data enhance player movement realism, enriching the VR experience.
Development Process
Research
Planning
Designing
Development
Maintenance
Tools/Technologies
Technical Achievements
NLP and AI Services: Key focus on NLP and AI functionalities, including:
