Tennis Video 2.0: A new presentation of sports videos with content separation and rendering
Journal of Visual Communication and Image Representation
Multimedia Tools and Applications
Generalized playfield segmentation of sport videos using color features
Pattern Recognition Letters
Tennis real play: an interactive tennis game with models from real videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Intelligent trainee behavior assessment system for medical training employing video analysis
Pattern Recognition Letters
Semantic scalability using tennis videos as examples
Multimedia Tools and Applications
Recognizing tactic patterns in broadcast basketball video using player trajectory
Journal of Visual Communication and Image Representation
Visible and infrared image registration in man-made environments employing hybrid visual features
Pattern Recognition Letters
Visible and infrared image registration employing line-based geometric analysis
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
A personal look back at twenty years of research in multimedia content analysis
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special Sections on the 20th Anniversary of ACM International Conference on Multimedia, Best Papers of ACM Multimedia 2012
Recognizing jump patterns with physics-based validation in human moving trajectory
Journal of Visual Communication and Image Representation
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This paper addresses the automatic analysis of court-net sports video content. We extract information about the players, the playing-field in a bottom-up way until we reach scene-level semantic concepts. Each part of our framework is general, so that the system is applicable to several kinds of sports. A central point in our framework is a camera calibration module that relates the a-priori information of the geometric layout in the form of a court model to the input image. Exploiting this information, several novel algorithms are proposed, including playing-frame detection, players segmentation and tracking. To address the player-occlusion problem, we model the contour map of the player silhouettes using a nonlinear regression algorithm, which enables to locate the players during the occlusions caused by players in the same team. Additionally, a Bayesian-based classifier helps to recognize predefined key events, where the input is a number of real-world visual features. We illustrate the performance and efficiency of the proposed system by evaluating it for a variety of sports videos containing badminton, tennis and volleyball, and we show that our algorithm can operate with more than 91% feature detection accuracy and 90% event detection.