M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Real-Time Multi-View Face Detection
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Parallel Tracking of All Soccer Players by Integrating Detected Positions in Multiple View Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Tracking Soccer Players using the Graph Representation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A 3D reconstruction and enrichment system for broadcast soccer video
Proceedings of the 12th annual ACM international conference on Multimedia
Tracking Multiple Occluding People by Localizing on Multiple Scene Planes
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A joint system for person tracking and face detection
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soccer ball detection by comparing different feature extraction methodologies
Advances in Artificial Intelligence
Accurate ball detection in soccer images using probabilistic analysis of salient regions
Machine Vision and Applications
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As a special application of computer vision, automatic sports video analysis has been studied by some researchers. This sports video analysis via computer vision is a moderately challenging problem: it is more difficult than analyzing a video of a few laboratory members acting as in a simple scenario and is easier than analyzing a video of crowded people at a subway station. So the success of an analysis heavily depends on how much one can exploit the prior information on the sport and setting. The most challenging and important part would be the tracking of players (and ball). With a multi-camera system, 3D tracking is feasible which is much more meaningful than 2D tracking for the analysis. As an initial step of 3D player tracking from multi-view soccer videos, this paper deals with automatic initialization of player positions. Initial 3D positions can be estimated by exploiting some conditions of a soccer match. To make it robust, prior knowledge on the features of players is learnt by support vector machines (SVM). Experimental results show that the proposed system is efficient for general soccer sequences.