Skip to main content
SpringerLink
Log in

Effectively Discriminating Fighting Shots in Action Movies

  • Short Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Fighting shots are the highlights of action movies and an effective approach to discriminating fighting shots is very useful for many applications, such as movie trailer construction, movie content filtering, and movie content retrieval. In this paper, we present a novel method for this task. Our approach first extracts the reliable motion information of local invariant features through a robust keypoint tracking computation; then foreground keypoints are distinguished from background keypoints by a sophisticated voting process; further, the parameters of the camera motion model is computed based on the motion information of background keypoints, and this model is then used as a reference to compute the actual motion of foreground keypoints; finally, the corresponding feature vectors are extracted to characterizing the motions of foreground keypoints, and a support vector machine (SVM) classifier is trained based on the extracted feature vectors to discriminate fighting shots. Experimental results on representative action movies show our approach is very effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Adams B, Dorai C, Venkatesh S. Novel approach to determining tempo and dramatic story sections in motion pictures. In Proc. the 7th IEEE International Conference on Image Processing, Vancouver, Canada, Sept. 10-13, 2000, pp. 283–286.

  2. Liu A, Li J, Zhang Y, Tang S, Song Y, Yang Z. An innovative model of tempo and its application in action scene detection for movie analysis. In Proc. the 9th IEEE Workshop on Applications of Computer Vision, Copper Mountain, USA, Jan. 7-9, 2008, pp. 1–6.

  3. Chen H W, Kuo J H, Chu W T, Wu J L. Action movies segmentation and summarization based on tempo analysis. In Proc. the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, USA, Oct. 15-16, 2004, pp. 251–258.

  4. Chen L, Ozsu M T. Rule-based scene extraction from video. In Proc. the 9th IEEE International Conference on Image Processing, Rochester, USA, Sept. 22-25, 2002, pp. 737–740.

  5. Cheng W, Liu C, Xu D. An approach to action scene detection in martial arts movies. Acta Electronica Sinica, 2006, 34(5): 915–920.

    Google Scholar 

  6. Gong Y, Wang W Q, Jiang S Q, Huang Q M, Gao W. Detecting violent scenes in movies by auditory and visual cues. In Proc. the Paci¯c-Rim Conference on Multimedia, Tainan, China, Dec. 9-13, 2008, pp. 317–326.

  7. Datta A, Shah M, Lobo N D V. Person-on-person violence detection in video data. In Proc. the 16th International Conference on Pattern Recognition, Quebec, Canada, Aug. 11-15, 2002, pp. 433–438.

  8. Mecocci A, Micheli F. Real-time automatic detection of violent-acts by low-level colour visual cues. In Proc. the 14th IEEE International Conference on Image Processing, San Antonio, USA, Sept. 16-19, 2007, pp. 345–348.

  9. Wang S, Jiang S, Huang Q, Gao W. Shot classification for action movies based on motion characteristics. In Proc. the 15th IEEE International Conference on Image Processing, San Diego, USA, Oct. 12-15, 2008, pp. 2508–2511.

  10. Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91–110.

    Article  Google Scholar 

  11. Battiato S, Gallo G, Puglisi G, Scellato S. SIFT features tracking for video stabilization. In Proc. the 14th International Conference on Image Analysis and Processing, Modena, Italy, Sept. 10-14, 2007, pp. 825–830.

  12. Kraaij W, Ianeva T. TRECVID-2005 low-level (camera motion) feature task. http://www.nlpir.nist.gov/projects/tvpubs/tv5.papers/tv5.llf.slides.final.pdf, 2005.

  13. Fan R, Chen P, Lin C. Working set selection using the second order information for training SVM. Journal of Machine Learning Research, 2005, 6(12): 1889–1918.

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Beijing, 100049, China

    Shu-Gao Ma & Wei-Qiang Wang (Member, ACM, IEEE)

  2. Computer Science Department, Boston University, Boston, MA, 02215, U.S.A.

    Shu-Gao Ma

  3. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Wei-Qiang Wang (Member, ACM, IEEE)

Authors
  1. Shu-Gao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei-Qiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei-Qiang Wang.

Additional information

This work was supported in part by the National High Technology Research and Development 863 Program of China under Grant No. 2006BAH02A24-2 and by the National Natural Science Foundation of China under Grant No. 60873087.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 84 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ma, SG., Wang, WQ. Effectively Discriminating Fighting Shots in Action Movies. J. Comput. Sci. Technol. 26, 187–194 (2011). https://doi.org/10.1007/s11390-011-9425-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-011-9425-6

Keywords

Search

Navigation