An Intelligent Tracking System to Analyze Shooting Angles Compared to NBA Players using AI and Machine Learning

This project aims to solve the problem of providing real-time, personalized feedback on basketball shooting form using machine learning (ML). By comparing a user’s body angles during their shot to those of professional players, the program delivers tailored suggestions for improvement. The core technologies used include pose detection through computer vision and a machine learning model that analyzes and compares joint angles. Challenges included fine-tuning the model’s confidence score to ensure accurate comparisons between users and pros, handling image quality issues, and providing clear feedback to users of different skill levels. The experiments showed that when professional players were compared to themselves, the system returned very high similarity scores, confirming the model’s accuracy.The project stands out because of its personalized feedback feature, helping both beginner and advanced users improve their shooting form. By addressing common limitations such as image quality and skill variability, this tool offers a unique solution for athletes looking to refine their performance.