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@article{196082,
        author = {Manish Patil and Pradumna Patil and Aadinath Khomane and Akash Baviskar and Prof. Pallavi Bangale},
        title = {Computer Vision for Biomechanical Analysis in Cricket: A Survey of Techniques, Challenges, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13850-13858},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196082},
        abstract = {Using quantitative methods to analyze sports biomechanics is now essential for enhancing athletic output and reducing injury potential. For a sport like cricket, which is defined by intricate and rapid movements, the need for scalable and objective analytical tools is especially high. This paper delivers a thorough review of current computer vision and machine learning applications within cricket’s biomechanical analysis, concentrating specifically on batting. We offer a formal classification of the current body of work, organizing studies by their primary objectives, such as correcting technique, analyzing performance, or predicting injury. This review also charts a clear technological progression, detailing the field’s shift from intrusive, wearable sensors to non-invasive, vision-based systems driven by deep learning and pose estimation. The dominant methodologies are critically evaluated, along with their respective contributions and inherent drawbacks. From this analysis, we pinpoint key trends and persistent obstacles—like model generalizability and a scarcity of high-quality data—as well as promising avenues for future work, including 3D pose modeling, multi-modal sensor fusion, and Explainable AI (XAI). This survey acts as a foundational reference for academics and industry professionals, offering a systematic overview of the discipline and pinpointing open questions for further research.},
        keywords = {Survey, Cricket Biomechanics, Pose Estimation, Computer Vision, Injury Prevention, Machine Learning, Sports Analytics, Literature Review.},
        month = {April},
        }