Crick-net: A Convolutional Neural Network based Classification Approach for Detecting Waist High No Balls in Cricket

TL;DR

This paper presents Crick-net, a CNN-based approach using Inception V3 to automatically detect waist high no balls in cricket, achieving 88% accuracy, thereby aiding automated decision-making in the sport.

Contribution

The paper introduces a novel CNN-based method specifically designed for waist high no ball detection in cricket, leveraging Inception V3 architecture.

Findings

Achieved 88% accuracy in no ball detection

Demonstrated effectiveness of CNNs in cricket event analysis

Reduced error rate compared to manual decisions

Abstract

Cricket is undoubtedly one of the most popular games in this modern era. As human beings are prone to error, there remains a constant need for automated analysis and decision making of different events in this game. Simultaneously, with advent and advances in Artificial Intelligence and Computer Vision, application of these two in different domains has become an emerging trend. Applying several computer vision techniques in analyzing different Cricket events and automatically coming into decisions has become popular in recent days. In this paper, we have deployed a CNN based classification method with Inception V3 in order to automatically detect and differentiate waist high no balls with fair balls. Our approach achieves an overall average accuracy of 88% with a fairly low cross-entropy value.