Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning

Motion capture technology has been used in many sports analytics, particularly for analyzing player movements in games like cricket. However, the high cost of commercial systems and the disturbing nature of wearable devices limit their accessibility and practicality. This study presents a two-fold solution: first, the development of a low-cost, non-intrusive optical motion capture system; and second, the implementation of a machine learning model to classify cricket batting shots based on body angle variations over time. After evaluating multiple optical techniques, MediaPipe was identified as the most effective and affordable solution. A system comprising four Logitech 720p cameras was deployed using Python multiprocessing to capture player motion in real time, ensuring efficient handling of both computational and input/output operations. Motion data collected from the system was analyzed to extract distinct angle variation patterns associated with different batting shots. These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The proposed model achieved an accuracy of 95% in classifying various batting shots, demonstrating the feasibility and effectiveness of the system as an economical alternative for sports motion analysis.