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Violence Video Detection by Discriminative Slow Feature Analysis

  • Kaiye Wang
  • Zhang Zhang
  • Liang Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

Nowadays, Internet makes it easy for us to share all kinds of information. However, violent content in web has harmful influence on those who lack proper judgment, especially teenagers. This paper presents an approach for detecting violence in videos, where Discriminative Slow Feature Analysis (D-SFA) is introduced to learn slow feature functions from dense trajectories derived from videos. Afterwards, with the learnt slow feature functions, the Accumulated Squared Derivative (ASD) features are extracted to represent videos. Finally, a linear support vector machine (SVM) is trained for classification. We also construct a Violence Video (VV) dataset which includes 200 violence samples and 200 non-violence samples collected from Internet and movies. The experimental results on the newly established dataset demonstrate the effectiveness of the proposed method.

Keywords

violence detection discriminative slow feature analysis dense trajectories 

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References

  1. 1.
    Giannakopoulos, T., Kosmopoulos, D.I., Aristidou, A., Theodoridis, S.: Violence Content Classification Using Audio Features. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds.) SETN 2006. LNCS (LNAI), vol. 3955, pp. 502–507. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Giannakopoulos, T., Pikrakis, A., Theodoridis, S.: A Multi-Class Audio Classification Method With Respect To Violent Content In Movies Using Bayesian Networks. In: Proceedings of the 9th International Workshop on Multimedia Signal Processing, pp. 90–93. IEEE Press, Crete (2007)CrossRefGoogle Scholar
  3. 3.
    Gong, Y., Wang, W.-Q., Jiang, S., Huang, Q., Gao, W.: Detecting Violent Scenes in Movies by Auditory and Visual Cues. In: Huang, Y.-M.R., Xu, C., Cheng, K.-S., Yang, J.-F.K., Swamy, M.N.S., Li, S., Ding, J.-W. (eds.) PCM 2008. LNCS, vol. 5353, pp. 317–326. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Lin, J., Wang, W.: Weakly-Supervised Violence Detection in Movies with Audio and Video Based Co-training. In: Muneesawang, P., Wu, F., Kumazawa, I., Roeksabutr, A., Liao, M., Tang, X. (eds.) PCM 2009. LNCS, vol. 5879, pp. 930–935. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Giannakopoulos, T., Makris, A., Kosmopoulos, D., Perantonis, S., Theodoridis, S.: Audio-Visual Fusion for Detecting Violent Scenes in Videos. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS, vol. 6040, pp. 91–100. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Wiskott, L., Sejnowski, T.: Slow Feature Analysis: Unsupervised Learning of Invariances. Neural Computation 14, 715–770 (2002)zbMATHCrossRefGoogle Scholar
  7. 7.
    Zhang, Z., Tao, D.: Slow Feature Analysis for Human Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 436–450 (2012)CrossRefGoogle Scholar
  8. 8.
    Wang, H., Klaser, A., Schmid, C., Liu, C.L.: Action recognition by dense trajectories. In: Proceedings of Computer Vision and Pattern Recognition, pp. 3169–3176. IEEE Press, Providence (2011)Google Scholar
  9. 9.
    Farnebäck, G.: Two-Frame Motion Estimation Based on Polynomial Expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363–370. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Chang, C.C., Lin, C.J.: LIBSVM:A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 27, 27:1–27:27 (2001)CrossRefGoogle Scholar
  11. 11.
    Laptev, I., Lindeberg, T.: Space-time Interest Points. In: Proceedings of International Conference on Computer Vision, pp. 432–439. IEEE Press, Nice (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kaiye Wang
    • 1
  • Zhang Zhang
    • 2
  • Liang Wang
    • 2
  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina
  2. 2.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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