Soccer video analysis by ball, player and referee tracking
SAICSIT '06 Proceedings of the 2006 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Live 3D Video in Soccer Stadium
International Journal of Computer Vision
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
ACM SIGGRAPH 2008 new tech demos
Determining Radius and Position of a Sphere from a Single Catadioptric Image
Journal of Intelligent and Robotic Systems
Players Clustering Based on Graph Theory for Tactics Analysis Purpose in Soccer Videos
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Recovering ball motion from a single motion-blurred image
Computer Vision and Image Understanding
Ball route estimation under heavy occlusion in broadcast soccer video
Computer Vision and Image Understanding
Playfield and ball detection in soccer video
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Non-goal scene analysis for soccer video
Neurocomputing
Self-calibration based 3d information extraction and application in broadcast soccer video
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
A scheme for ball detection and tracking in broadcast soccer video
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Take your eyes off the ball: Improving ball-tracking by focusing on team play
Computer Vision and Image Understanding
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In soccer games, understanding the movement of players and a ball is essential for the analysis of matches or tactics. In this paper, we present a system to track players and a ball and to estimate their positions from video images. Our system track players by extracting shirt and pants regions and can cope with the posture change and occlusion by considering their colors, positions, and velocities in the image. The system extracts ball candidates by using the color and motion information, and determines the ball among them based on motion continuity. To determine the player who is holding the ball, the position of players on the field and the 3D position of the ball are estimated. The ball position is estimated by fitting a physical model of movement in the 3D space to the observed ball trajectory. Experimental results on real image sequences show the effectiveness of the system.