Goalmouth Detection in Field-Ball Game Video Using Fuzzy Decision Tree

On one hand, goalmouth detection is a kind of high-level semantic concept detection methods in sports video, on the other hand, data mining for video processing that can benefit video analysis is a promising research frontier. In this paper, a robust method is proposed to detect the presence of goalmouth in field-ball game video based on fuzzy decision trees. Balance process is added in training procedure. The experiment result shows that our algorithm can improve the classification when comparing with the threshold based algorithm and the decision tree based algorithm. Fuzzy rules can also be easily deduced from the constructed tree to interpret the classification model.

[1]  B. Bouchon-Meunier,et al.  An adaptable system to construct fuzzy decision trees , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[2]  Ling Guan,et al.  Indexing of NFL video using MPEG-7 descriptors and MFCC features , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[3]  Wei-Ta Chu,et al.  Semantic units detection and summarization of baseball videos , 2004, The 2004 47th Midwest Symposium on Circuits and Systems, 2004. MWSCAS '04..

[4]  Changsheng Xu,et al.  Real-time camera field-view tracking in soccer video , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[5]  Lei Chen,et al.  The Research and Application of Human Detection Based on Support Vector Machine Using in Intelligent Video Surveillance System , 2008, 2008 Fourth International Conference on Natural Computation.

[6]  Alan F. Smeaton,et al.  A query description model based on basic semantic unit composite petri-nets for soccer video analysis , 2004, MIR '04.

[7]  Tsung-Han Tsai,et al.  A robust shot change detection method for content-based retrieval , 2005, 2005 IEEE International Symposium on Circuits and Systems.

[8]  Jun Luo,et al.  Person-Specific SIFT Features for Face Recognition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[9]  Christophe Marsala,et al.  Fuzzy data mining for video , 2003, EUSFLAT Conf..

[10]  Changsheng Xu,et al.  Robust goal-mouth detection for virtual content insertion , 2003, MULTIMEDIA '03.

[11]  Xinguo Yu,et al.  Current and Emerging Topics in Sports Video Processing , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[12]  Hanqing Lu,et al.  Shot Classification in Broadcast Soccer Video , 2008 .

[13]  Christophe Marsala,et al.  UPMC-LIP6 at TrecVid'08: Balanced and Unbalanced Forests of Fuzzy Decision Trees for High-Level Feature Detection , 2008, TRECVID.

[14]  Jiawei Han.  Data mining for image/video processing: a promising research frontier , 2008, CIVR '08.

[15]  Bao-Chang Pan,et al.  Motion object detection of video based on principal component analysis , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[16]  Shih-Fu Chang,et al.  Structure analysis of soccer video with hidden Markov models , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[17]  Yaser Sheikh,et al.  Model generation for video-based object recognition , 2006, MM '06.

[18]  L. Zadeh.  Probability measures of Fuzzy events , 1968 .