Problem
Research questions and friction points this paper is trying to address.
Illegal Bowling Action Detection
Cricket
Real-time Monitoring
Computer Vision
Fair Play
Deep Learning Based Illegal Bowling Action Detection
🤖 AI Summary
This study addresses the challenges of real-time detection of illegal bowling actions in cricket and the limitations of conventional sensor-based approaches by proposing a novel artificial intelligence method grounded in computer vision and deep learning. The proposed approach extracts key frames from video sequences to quantitatively analyze angular variations of the bowling arm, subsequently identifying rule violations through threshold-based evaluation. Experimental results demonstrate that the model achieves a high true positive rate on a custom-built dataset, validating its potential for application in real-time umpiring assistance scenarios. This work provides a new non-invasive, automated solution for compliance monitoring in cricket, offering a promising alternative to existing detection paradigms.
📝 Abstract
Cricket, often referred to as the "gentleman's game," adheres to a strict rule set for both batsmen and bowlers, where each delivery can significantly impact the match outcome. Detecting illegal bowling actions is crucial for maintaining fair play, yet it remains challenging for umpires to monitor in real time. Existing sensor-based solutions have limitations in live match scenarios, making real-time assessment difficult. This paper proposes a computer vision-based deep learning solution to detect illegal bowling actions in live cricket matches. To develop and evaluate our approach, we compiled a dataset of 62 videos featuring 11 male bowlers, capturing both legal and illegal bowling actions from multiple angles-front, back, and side. However, the dataset predominantly comprises right-handed bowlers with conventional actions. The proposed system identifies two key frames, the shoulder frame and the release frame from video footage of a bowler's delivery and analyzes the change in the bowling arm's angle between these frames. If the angle difference exceeds a predefined threshold (e.g., 15 degrees), the delivery is flagged as potentially illegal. We evaluated the system on a custom dataset and achieved a high true positive rate, suggesting the system's potential effectiveness in real-time match settings. However, further research is required to validate the system across diverse environmental conditions and larger datasets to ensure generalizability and robustness in various live match scenarios. To the best of our knowledge, this is the first AI-based computer vision method for detecting illegal bowling actions in cricket.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Computer Vision
Deep Learning
Illegal Bowling Action Detection
Key Frame Extraction
Cricket
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