Automation of Cricket Scoreboard by Recognizing Umpire Gestures

Imagine how tiresome it is for the scorers to update the scoreboard after each ball delivery during a cricket match. They need to be alert during any point in the match, watch every single ball, record ball by ball events, modify the score and coordinate with the umpire the entire time. A system that can update the scoreboard automatically after every ball will lessen their effort by half; the time taken for the updation and the chances of errors will also be reduced. A novel method for umpire pose detection for updating the cricket scoreboard during real-time cricket matches is suggested in this work. The proposed system identifies the events happening in the pitch by recognizing the gestures of the umpire and then updates the scoreboard accordingly. The concept of transfer learning is used to accelerate the training of neural network for feature extraction. The Inception V3 network pretrained on the visual database ImageNet is culled as the primary prospect for feature extraction. Instead of initializing the model with random weights, initializing it with the pretrained weights reduces the training time and hence is more efficient. The proposed system is a combination of two SVM