Real-Time AI Driven Shooting Posture Assessment and Correction for Professional and Military Training Using Machine Learning, OpenCV, and MediaPipe

Shooting is one of the most popular Olympic Games in India As for now Shooting contributed 4 out of 35 Olympic medals for the country. Thus, the presented paper aims to expose some advanced training methodologies in order to achieve the maximum potential of this kind of sport. This paper also describes an implementation of an AI-based system for shooting posture evaluation and feedback by employing computer vision and sophisticated posture recognition methods in real-time control system. For image acquisition and processing, the system incorporates OpenCV and, for body landmarks detection, it uses MediaPipe's pose estimation models. It therefore computes outstanding angles as well as changes in relation to these postures to allow users to correct their positioning. Refinement of the training process in the usage of weapons is evident by adoption of the proposed solution that has displayed efficient concerning the professional shooters, military personnel, and the target consumers. The ability of the system to give real-time accurate feedback on the participants' posture while at the same time increasing the training quality and performance efficiency minimizes the gap between conventional and contemporary technological approaches in shooting training. This research also discusses the utility of the system in multiple shooting disciplines and situations. Through the use of real-time video data and through the utilization of sophisticated machine learning algorithms, the system is able to tailor feedback depending on the shooting position or shooting posture or tactic that the user signifies to the system. Thus, the proper combination of real-time posture assessment guarantees that users get necessary feedback instantly and apply it in the process of enhancing their technique. The research therefore establishes the role of the system in enhancing training efficiency and effectiveness; though this study could be relevant to other fields such as security, the application in shooting sports when AI and computer vision are integrated advances the sector immensely.[2]