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@article{178253,
        author = {Pragathi V and Swathi A K and Priya Dharshini N S and Sreeranjani P and Sasikala P},
        title = {Sports Analysis Software},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3210-3216},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178253},
        abstract = {The increasing integration of artificial intelligence into sports has revolutionized performance analysis, enabling deeper insights into team and player behavior. This project introduces a lightweight yet powerful Sports Analysis Software designed for coaches and analysts to evaluate their team’s performance with ease. The application incorporates state-of-the-art computer vision algorithms—YOLOv5 for object detection and DeepSort for tracking—to automate player detection and movement analysis from video footage. Key features include automated offense and defense classification, lineup builder, post-game data breakdown, and graph-based performance summaries. Users can import match videos locally or via cloud or YouTube, enhancing accessibility and flexibility. Additionally, the tool allows for custom dataset training, empowering teams to personalize the model for higher accuracy. With a user-friendly interface and extensible architecture, the software streamlines game analysis, contributing to more informed coaching strategies and improved athletic outcomes.},
        keywords = {Sports analytics, YOLOv5, DeepSort, Object detection, Player tracking, AI in sports, Lineup builder, Video analysis, Custom dataset training.},
        month = {May},
        }