Journal Paper

Detecting balls in sports plays a pivotal role in enhancing game analysis, providing real-time data for spectators, and improving decision-making and strategic thinking for referees and coaches. This is a highly debated and researched topic, but most works focus on one sport. Effective generalization of a single method or algorithm to different sports is much harder to achieve. This article reviews methodologies and advancements in object detection tailored to ball detection across various sports. Traditional computer vision techniques and modern deep learning methods are visited, emphasizing their strengths, limitations, and adaptability to diverse game scenarios. The challenges of occlusion, dynamic backgrounds, varying ball sizes, and high-speed movements are identified and discussed. This review aims to consolidate existing knowledge, compare state-of-the-art detection models, highlight pivotal challenges and possible solutions, and propose future research directions. The article underscores the importance of optimizations for accurate and efficient ball detection, setting the foundation for next-generation sports analytics systems.