Abstract
In this paper, we propose a fully automatic and computationally efficient algorithm for analysis of sports videos. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.
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References
Assfalg, J., Bertini, M., Colombo, C., Bimbo, A.D., Nunziati, W.: Semantic annotation of soccer videos: automaitc highlights identification. Computer Vision and Image Understanding (2004)
Belongie, S., Carson, C., Greenspan, H., Malik, J.: Color- and Texture-Based Segmentation Using EM and Its Application to Content-Based Image Retrieval. In: IEEE International Conference Computer Vision, pp. 675–682 (1998)
Duan, L., Xu, M., Chua, T., Tian, Q., Xu, C.: A mid-level representation framework for semantic sports video analysis. In: Proc. of ACM MM 2003, pp. 33–44 (2002)
Fan, J., Gao, Y., Luo, H.: Multi-level annotation of natural scenes using dominant image components and semantic concepts. In: ACM Multimedia (ACM MM 2004), October 10-16, pp. 540–547 (2004)
Mentzelopoulos, M.,, P.: Key-frame Extraction Algorithm using Entropy Difference. In: Proc. of the 6th ACM SIGMMA International Workshop on Multimedia Information Retrieval (MIR 2004), pp. 39–45 (2004)
Rissanen, J.: Hypothesis selection and testing by the mdl principle. The Computer Journal 42(4) (1999)
Jiang, S., Ye, Q., Gao, W., Huang, T.: A new method to segment playfield and its applications in match analysis in sports videos. In: ACM MM (2004)
Smith, J.: Image classification and querying using composite region templates. Computer Vision and Image Understanding 75 (1999)
Tong, X., Liu, Q., Duan, L., Lu, H., Xu, C., Tian, Q.: A unified framework for semantic shot representation of sports video. In: ACM Multimedia Information Retrieval MIR, November 10-11, pp. 127–134 (2005)
Yan, F., Christmas, W., Kittler, J.: A tennis ball tracking algorithm for automatic annotation of tennis match. In: BMVC 2005, vol. 2, pp. 619–628 (2005)
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© 2011 Springer-Verlag Berlin Heidelberg
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Mentzelopoulos, M., Psarrou, A., Angelopoulou, A. (2011). An Unsupervised Method for Active Region Extraction in Sports Videos. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_6
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DOI: https://doi.org/10.1007/978-3-642-21498-1_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21497-4
Online ISBN: 978-3-642-21498-1
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