Where Are the Ball and Players? Soccer Game Analysis with Color Based Tracking and Image Mosaick
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Robust Tracking of Soccer Players Based on Data Fusion
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Tracking Players and Estimation of the 3D Position of a Ball in Soccer Games
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
A ball tracking framework for broadcast soccer video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Multiple Objects Tracking with Multiple Hypotheses Graph Representation
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Tracking and labelling of interacting multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
A review of vision-based systems for soccer video analysis
Pattern Recognition
A multiple camera methodology for automatic localization and tracking of futsal players
Pattern Recognition Letters
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This paper deals with player tracking in broadcast soccer video. In soccer games, players sometimes gather in a small area in the case of, for example, a corner kick. In such a case, due to a heavy occlusion, a simple detection-and-tracking method will certainly fail. We cope with such difficult cases using a constraint satisfaction approach. To integrate pieces of evidence at various places and frames, we construct a graph of player blobs representing possible player transitions. The view of each blob provides a constraint on the number of players in the blob. All such constraints are propagated through the graph to reduce the ambiguities in the numbers. The remaining ambiguities after the propagation is handled by a statistical approach in which a set of the most likely interpretations on the numbers is selected. Finally the players' trajectory are determined based on their smoothness. Experimental results show the effectiveness of the method.