🏁 Determine whether or not a player makes or misses a basketball shot with Computer Vision.
The Shot Tracker project is an advanced computer vision application that aims to accurately determine whether a person makes or misses a basketball shot. It is designed to help hoopers track their own makes and misses when shooting arouund. The project leverages YOLO v8, python, an real time vide analyis to automate the tracking for makes and misses.
🔑 Key Features
- Ball Tracking: The project employs object detection by training our own dataset to identify and track the positions of the basketball.
- Rim Tracking: Like the ball Tracking, with personalized pre-trained modeling, we are able to identify where the rim is at all times.
- Real-Time Video Analysis: The system can process live video feeds from soccer matches, enabling real-time offside detection during gameplay. It can also be applied to pre-recorded matches for analysis and review.
🚀 Further Uses
- Shot Percentage by Area: Although not complete, if enough training data were to be aquired, a map of the basketball court with different zones can be used to determine a player's shooting percentage from differnt zones.
💻 Technology
- OpenCV
- NumPy
- YoloV8