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Volume 14 - 2018

No.1 January 2018
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Player Detection based on Support Vector Machine in Football Videos

Volume 14, Number 2, February 2018, pp. 309-319
DOI: 10.23940/ijpe.18.02.p12.309319

Chengjun Cui

Department of Physical Education, Nanjing University of Science and Technology, Nanjing, 210094, China


An automatic player detection method based on fuzzy decision making one-class SVM is proposed. Detection results of statistical classifier player detection methods are better than rule based player detection methods. However, manually labelled training samples are used in these statistical classifiers based player detection methods. Thus, cost is very important. To resolve this problem, we propose an instinctive player detection method using fuzzy decision making one-class SVM and automatically collected player samples. In this method, one-class SVM (OCSVM) is introduced to train the player detector by drawing lessons from the human object category classification mechanism. Additionally, decision function of OCSVM is improved by dividing the decision value dynamically using the fuzzy decision method, which is able to reduce the detection error caused by the insufficient representativeness of the automatically collected training samples. Finally, a set of criteria is introduced to obtain the training samples automatically, and player detection experiments are performed on these training samples using FD-OCSVM. Experiments show that better detection results are obtained using the proposed method in the scenario of using automatically collected training samples, which improves the automatic degree of player detection.


References: 12

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    3. Llach J and Bhagavathy S and Huang Y, “Players and Ball Detection in Soccer Videos Based on Color Segmentation and Shape Analysis”, International Workshop on Multimedia Content Analysis and Mining, Weihai, China: Springer, vol.6, pp. 416–425, 2007.
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    7. Qiang Liu and Ye Zhang, “The Specificity of Category Information Processing in the Visual System”, Advances in Psychological Science, vol.19, no.1, pp.42- 49, 2011.
    8. Moya M and Hush R, “Network Constraints and Multi-Objective Optimization for One-class Classification”, Neural Networks, vol.9, no.3, pp.463–474, 1996.
    9. Kanwisher N, “What’s in a Face”, Science Magazine, vol.311, no.5761, pp.617–618, 2006.
    10. Kanwisher N, “A Window into the Functional Architecture of the Mind”, Proceedings of the National Academy of Sciences of the United States of America, vol.107, no.25, pp.11163–11170, 2010.
    11. Huang Qu and Gi Zhu, “Event Tactic Analysis Based on Broadcast Sports Video”, IEEE Transactions on Multimedia, vol.11, no.1, pp.49 –67, 2009.
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