Abstract
In the process of behavior recognition of multiplayers for soccer game video, various features of athletes need to be extracted. In this paper, color moments extracted by using color classification learning set are regarded as color feature. Contour features of athletes are extracted by utilizing players silhouettes block extraction and normalization. Hough transform is used to extract the features of coordinates of pitch line, which can be used for camera calibration, rebuilding the stadium, and calculating the coordinate of players in the real scene. The trajectories of players and ball are predicted by using Kalman filter, while trajectories characteristics of player and ball are extracted by using the trajectory growth method. Temporal and spatial interest points are extracted in this paper. Experimental results show that the accuracy of behavior recognition can be greatly improved when these features extracted are used to recognize athlete behavior.
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Acknowledgments
The authors are very grateful for the support provided by the National Natural Science Foundation of China (61462008, 61751213, 61866004), the Key projects of Guangxi Natural Science Foundation (2018GXNSFDA294001, 2018GXNSFDA281009), the Natural Science Foundation of Guangxi (2017GXNSFAA198365), 2015 Innovation Team Project of Guangxi University of Science and Technology (gxkjdx201504), Scientific Research and Technology Development Project of Liuzhou (2016C050205).
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Wang, Z. et al. (2019). Characteristics Extraction of Behavior of Multiplayers in Video Football Game. In: Quinto, E., Ida, N., Jiang, M., Louis, A. (eds) The Proceedings of the International Conference on Sensing and Imaging, 2018. ICSI 2018. Lecture Notes in Electrical Engineering, vol 606. Springer, Cham. https://doi.org/10.1007/978-3-030-30825-4_11
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DOI: https://doi.org/10.1007/978-3-030-30825-4_11
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