Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling

• Published in: IEEE transactions on circuits and systems for video technology Vol. 18; no. 11; pp. 1628 - 1638
• Main Authors: Jungong Han, Farin, D., de With, P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3-D modeling; Bayesian methods; Broadcasting; Calibration; Cameras; Computer vision; content analysis; Data mining; Event detection; feature extraction; Image segmentation; moving object; multi-level analysis; Multimedia communication; Solid modeling; sports video; Studies
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2008.2005611

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.