A framework for automatic detection of soccer goal event based on cinematic template

As digital sports video data become more and more pervasive, automatic highlight detection is particularly demanding. We present an automatic and effective framework for goal event detection based on the cinematic features. The proposed framework includes 4 parts: shot boundary detection, shot classification, slow-motion detection, and goal event detection. The shot boundary detector employs the multi-filter structure. Then, the goal detector uses the slow-motion replay to locate the goal shot and the new cinematic template to model and identify the goal event. By the experiments over diverse soccer game videos, the proposed framework is effective and efficient to find the goal shot.

[1]  Min Chen,et al.  DETECTION OF SOCCER GOAL SHOTS USING JOINT MULTIMEDIA FEATURES AND CLASSIFICATION RULES , 2003 .

[2]  Shih-Fu Chang,et al.  Algorithms and system for segmentation and structure analysis in soccer video , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[3]  A.M. Tekalp,et al.  Robust dominant color region detection and color-based applications for sports video , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[4]  David S. Doermann,et al.  Detection of slow-motion replay sequences for identifying sports videos , 1999, 1999 IEEE Third Workshop on Multimedia Signal Processing (Cat. No.99TH8451).

[5]  Peter J. L. van Beek,et al.  Detection of slow-motion replay segments in sports video for highlights generation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[6]  O. H. Lowry.  Academic press. , 1972, Analytical chemistry.

[7]  A. Murat Tekalp,et al.  Framework for tracking and analysis of soccer video , 2002, IS&T/SPIE Electronic Imaging.

[8]  Xinghua Sun,et al.  Bayesian-network-based soccer video event detection and retrieval , 2003, International Symposium on Multispectral Image Processing and Pattern Recognition.

[9]  A. Murat Tekalp,et al.  Temporal segmentation of video objects for hierarchical object-based motion description , 2002, IEEE Trans. Image Process..

[10]  Alberto Del Bimbo,et al.  Semantic annotation of soccer videos: automatic highlights identification , 2003, Comput. Vis. Image Underst..

[11]  Rangasami L. Kashyap,et al.  Video scene change detection method using unsupervised segmentation and object tracking , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[12]  Fumiko Satoh,et al.  Learning personalized video highlights from detailed MPEG-7 metadata , 2002, Proceedings. International Conference on Image Processing.