Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Fight Detection in Hockey Videos using Deep Network

  • Received : 2017.09.20
  • Accepted : 2017.12.01
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Understanding actions in videos is an important task. It helps in finding the anomalies present in videos such as fights. Detection of fights becomes more crucial when it comes to sports. This paper focuses on finding fight scenes in Hockey sport videos using blur & radon transform and convolutional neural networks (CNNs). First, the local motion within the video frames has been extracted using blur information. Next, fast fourier and radon transform have been applied on the local motion. The video frames with fight scene have been identified using transfer learning with the help of pre-trained deep learning model VGG-Net. Finally, a comparison of the methodology has been performed using feed forward neural networks. Accuracies of 56.00% and 75.00% have been achieved using feed forward neural network and VGG16-Net, respectively.

Keywords

References

  1. E. B. Nievas, O. D. Suarez, G. B. Garcia and R. Sukthankar, "Violence detection in video using computer vision techniques," In International conference on Computer analysis of images and patterns, pp. 332-339, Heidelberg, 2011.
  2. W. H. Cheng, W. T. Chu and J. L. Wu, "Semantic context detection based on hierarchical audio models," In Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval, pp. 109-115, 2003.
  3. T. Giannakopoulos, A. Makris, D. I. Kosmopoulos, S. J. Perantonis and S. Theodoridis, "Audio-Visual Fusion for Detecting Violent Scenes in Videos," In SETN, pp. 91-100, 2010.
  4. K. Guo, P. Ishwar and J. Konrad, "Action recogniti on from video using feature covariance matrices," IEEE Transactions on Image Processing. Vol. 22, No. 6, pp. 2479-2494, 2013. https://doi.org/10.1109/TIP.2013.2252622
  5. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar and L. Fei-Fei, "Large-scale video classification with convolutional neural networks," In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725-1732, 2014
  6. J. Melo, A. Naftel, A. Bernardino and J. Santos-Victor, "Detection and classification of highway lanes using vehicle motion trajectories," IEEE Transactions on intelligent transportation systems, Vol. 7, No. 2, pp.188-200, 2006. https://doi.org/10.1109/TITS.2006.874706
  7. C. Piciarelli and G. L. Foresti, "On-line trajectory clustering for anomalous events detection," Pattern Recognition Letters, vol. 27, no. 15, pp. 1835-1842, 2006. https://doi.org/10.1016/j.patrec.2006.02.004
  8. R. Poppe, "A survey on vision-based human action recognition". Image and vision computing, Vol. 28, No. 6, pp. 976-990, 2010. https://doi.org/10.1016/j.imavis.2009.11.014
  9. R. Saini, A. Ahmed, D. P. Dogra and P. P. Roy, "Classification of object trajectories represented by high-level features using unsupervised learning," In Proceedings of International Conference on Computer Vision and Image Processing, pp. 273-284, Singapore, 2017.
  10. R. Saini, A. Ahmed, D. P. Dogra and P. P. Roy, "Surveillance scene segmentation based on trajectory classification using supervised learning," In Proceedings of International Conference on Computer Vision and Image Processing, pp. 261-271, Singapore, 2017.
  11. R. Saini, P. Kumar, S. Dutta, P. P. Roy and U. Pal, "Local behavior analysis for trajectory classification using graph embedding," In 4th Asian Conference on Pattern Recognition, 2017. (accepted)
  12. R. Saini, P. Kumar, P. P. Roy and D. P. Dogra, "An efficient approach for trajectory classification using FCM and SVM". In IEEE Region 10 Symposium (TENSYMP), pp. 1-4, 2017.
  13. K. Simonyan and A. Zisserman, "Two-stream convolutional networks for action recognition in videos," In Advances in neural information processing systems, pp. 568-576, 2014.
  14. X. Wang, K. Tieu and E. Grimson, "Learning semantic scene models by trajectory analysis," Computer Vision-ECCV, pp.110-23, 2006.