How to Detect, Track, and Identify Basketball Players with Computer Vision

Basketball presents unique challenges for computer vision due to high-speed player movements, overlapping bodies, and similar uniforms, complicating player identification and tracking. The blog post outlines a comprehensive approach to overcoming these issues, using a suite of advanced models integrated into a single pipeline. RF-DETR is employed for object detection, SAM2 for video tracking and segmentation, and SigLIP with UMAP and K-means for unsupervised team clustering. For recognizing jersey numbers, SmolVLM2 and ResNet are used, with the latter achieving higher accuracy after fine-tuning. The pipeline is not optimized for real-time performance but demonstrates the potential of current models to tackle complex sports analytics tasks, focusing on tracking, identifying, and resolving player identities during live basketball games. While improvements are necessary, especially in number recognition under challenging conditions, the project provides a robust framework and open-source code for further experimentation and enhancement.