Cricket Shot Classification and Pose Correction using Detectron2 and XGBoost Classifier
Goutam Majumder, Tanul Khatri, Anubhav Tewari, Atharva Samel, Ganesh S. Khekare · Procedia Computer Science · 2025
Real-time sports analysis provides valuable insights to teams, allowing them to change their tactics and increase their winning probability through strategising. In India, the globalisation of sports, specifically Cricket, is at its peak, due to the Indian Premier League (IPL). In a match, a team’s performance heavily relies on the individual performances of its players. This research paper proposes a model that internally utilises Detectron2, an AI tool, for pose detection. This model generates key points that are then used to recognize various batting shots and utilize the eXtreme Gradient Boosting (XGBoost) Classifier, achieving an accuracy of 92.13%. Furthermore, comparisons between the methods proposed and other researched methods show outperforming them. The proposed method has been tested on a dataset containing four (4) types of shots: pull shot, sweep, drive, and leg glance flick.