Spatial Color Indexing and Applications
International Journal of Computer Vision
Computer Vision and Image Understanding
Semantic annotation of soccer videos: automatic highlights identification
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Structure analysis of soccer video with domain knowledge and hidden Markov models
Pattern Recognition Letters - Video computing
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Camera-based observation of football games for analyzing multi-agent activities
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Moving object segmentation by background subtraction and temporal analysis
Image and Vision Computing
Real-time view recognition and event detection for sports video
Journal of Visual Communication and Image Representation
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
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Automatic sport team discrimination, that is the correct assignment of each player to the relative team, is a fundamental step in high level sport video sequences analysis applications. In this work we propose a novel set of features based on a variation of classic color histograms called Positional Histograms: these features try to overcome the main drawbacks of classic histograms, first of all the weakness of any kind of relation between spectral and spatial contents of the image. The basic idea is to extract histograms as a function of the position of points in the image, with the goal of maintaining a relationship between the color distribution and the position: this is necessary because often the actors in a play field dress in a similar way, with just a different distribution of the same colors across the silhouettes. Further, different unsupervised classifiers and different feature sets are jointly evaluated with the goal of investigate toward the feasibility of unsupervised techniques in sport video analysis.