A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

• Published in: International journal of intelligent systems Vol. 37; no. 11; pp. 9620 - 9654
• Main Authors: Abulwafa, Arwa E., Saleh, Ahmed I., Saraya, Mohamed S., Ali, Hesham A.
• Format: Journal Article
• Language: English
• Published: New York Hindawi Limited 01.11.2022
• Subjects: ball detection; frame difference; Image processing; Intelligent systems; Mixture of Gaussian (MoG); Object recognition; optical flow; Optical flow (image analysis); Performance evaluation; Playgrounds; Soccer; Sports; sports video analysis; Strategy
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.22681

Sports video analysis has received much attention as it turned to be a hot research area in the field of image processing. This motivation offers opportunities that develop fascinating applications supported by analysis of different sports, especially soccer. Ball identification, in soccer images, is an essential task not only for goal‐scoring but also for performance evaluation. However, ball detection suffers from several hurdles such as occlusions, fast‐moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as Frame Difference, Mixture of Gaussian (MoG), Optical Flow, and so forth; ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB2D) strategy is proposed for identifying the ball through a set of image sequences extracted from a soccer match video. FB2D can accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB2D is compared to recent ball detection techniques. Experimental results show that FB2D outperforms recent detection techniques as it introduces both the highest level of detection accuracy in the testing stage and the lowest possible error.