CRICKET POSE PREDICTION USING DEEP LEARNING

Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes six strokes: pull, square-cut, cover drive, straight drive, flick, and sweep. The study uses the ResNet model to extract features from videos and to predict the strokes. The methodology involves data collection and pre-processing and using pre-trained model ResNet-34 CNN for cricket pose classification.The model captures video frames,preprocess them,predict the strokes.The study achieves an outstanding accuracy of 90.77% using the ResNet model, outperforming the other algorithms used in the study.. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality.