Abstract:
Cricket is a popular global sport that has many disciplines within it like
batting, balling, and fielding. The batsman plays a prominent role and over
the history game has produced many great batters. This research is focused
on the innovative real-time cricket shot classification and feedback system
for left-handed batsmen. The suggested methodology aims to address the gap
in professional training availability and cost-effective tools for technical
improvement for non-professional players. It mainly focuses on three basic
cricket strokes: front foot defense, back foot defense, and cover drive,
distinguishing between correct and incorrect executions. The model embeds
into a feedback system that can accurately classify the correct or incorrect
posture of these three shots while giving immediate feedback if the player is
playing a wrong shot by calculating the angles between key points using
algorithms. This method assists beginners by improving their skills on these
3 shots. The research involved creating a completely new data set of 58 lefthanded
players across different age groups. This completely new data set
consists of 1,960 videos, 490 per shot category, with correct and incorrect
shots captured from multiple angles from the offside of the batsman. A 2D
convolutional neural network (CNN) was developed for shot classification
with the Nadam optimizer. The model demonstrates exceptional performance
by achieving 97% accuracy on the test data set. Specifically, the system
exhibited low error rates with a 9.7% overall false positive rate and 1.04%
overall false negative rate. Further to that backfoot defense classification
resulted in a 0.00% false negative rate, a 2.45% false negative for cover drive
shot, and a 0.99% defensive false negative rate was demonstrated. Future
work on this would involve extending the system to increase the number of
cricket shots analyzed and enhancing the dataset to make the classification
broader and more accurate.
