Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

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



Abstract:

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

    1. Qe Huang and Gen Zhu, “Trajectory Based Event Tactics Analysis in Broadcast Sports Video”, International Conference on Multimedia. Santa Barbara, CA, USA: ACM, vol.4, pp.58–67, 2014.
    2. Konieczny J, Kurc M and Mackowiak S, “A Complex System for Football Player Detection in Broadcasted Video”, International Conference on Signals and Electronic Systems. Gliwice, Poland: IEEE, vol.6, pp.119–122, 2014.
    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.
    4. Dermott J, Chun M and Kanwisher N, “The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception”, The Journal of Neuro-science, vol.17, no.11, pp.4302–4311, 2007.
    5. Miura K and Utsumi O, “An Object Detection Method for Describing Soccer Games from Video”, International Conference on Multimedia and Expo. Lausanne, Switzerland: IEEE, vol.42, pp. 45–48, 2002.
    6. Jin Liu and Xe Tong, “Automatic Player Detection, Labeling and Tracking in Broadcast Soccer Video”, Pattern Recognition Letters, vol.30, no.2, pp.103 – 113, 2009.
    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.
    12. Tong Xin and Jin Liu, “Automatic Player Labeling, Tracking and Field Registration and Trajectory Mapping in Broadcast Soccer video”, ACM Transactions on Intelligent Systems and Technology, vol.2, no.2, pp.15–32,2013

       

      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      Attachments:
      Download this file (IJPE-2018-02-12.pdf)IJPE-2018-02-12.pdf[Player Detection based on Support Vector Machine in Football Videos]855 Kb
       

      CURRENT ISSUE

      Prev Next

      Semi-Supervised Extreme Learning Machine using L1-Graph

      Hongwei Zhao, Yang Liu, Shenglan Liu, and Lin Feng

      Read more

      Collision Analysis and an Efficient Double Array Construction Method

      Lianyin Jia, Wenyan Chen, Jiaman Ding, Xiaohui Yuan, Binglin Shen, and Mengjuan Li

      Read more

      A Measuring Method for User Similarity based on Interest Topic

      Yang Bai, Guishi Deng, Liying Zhang, and Yi Wang

      Read more

      Performance Analysis of Information Fusion Method based on Bell Function

      Meiyu Wang, Zhigang Li, Dongmei Huang, and Xinghao Guo

      Read more

      Two-Stage Semantic Matching for Cross-Media Retrieval

      Gongwen Xu, Lina Xu, Meijia Zhang, and Xiaomei Li

      Read more
      This site uses encryption for transmitting your passwords. ratmilwebsolutions.com