Optimizing Basketball Shot Trajectory using Image Segmentation Techniques for Training Feedback

In basketball, the efficiency of shot-making is crucial for success, especially in high-stakes games. Traditional self-training methods face significant challenges due to the absence of training equipment and personalized coaching. This paper presents an approach using computer vision and deep learning algorithms to provide feedback to players towards optimizing basketball shot trajectories, offering a solution for self-training. We employ image segmentation to accurately track the basketball and analyze shooting videos, enabling the extraction of critical parameters such as the release angle and shot trajectory. Our methodology integrates a Faster R-CNN model for object detection and introduces two novel parabolic curve fitting techniques: Bounce-Around and Sliding Window Sampling Consensus (SWISAC). These techniques allow for precise trajectory analysis and on-or-off-course predictions, despite occlusions by the net. Experimental results demonstrate the efficacy of our approach in providing actionable feedback for improving shooting accuracy. This research lays the groundwork for future advancements in automated sports analytics, enhancing and democratizing the training and performance feedback of basketball players.