Multimedia Systems

, Volume 22, Issue 3, pp 275–285 | Cite as

A fast recognition algorithm for suspicious behavior in high definition videos

  • Chundi Mu
  • Jianbin Xie
  • Wei YanEmail author
  • Tong Liu
  • Peiqin Li
Regular Paper


Detecting suspicious behavior from high definition (HD) videos is always a complex and time-consuming process. To solve that problem, a fast suspicious behavior recognition method is proposed based on motion vectors. In this paper, the data format and decoding features of HD videos are analyzed. Then, the characteristics of suspicious activities and the ways of obtaining motion vectors directly from the video stream are concluded. Besides, the motion vectors are normalized by taking the reference frames into account. The feature vectors that display the inter-frame and intra-frame information of the region of interest are extracted. Gaussian radial basis function is employed as the kernel function of the support vector machines (SVM). It also realizes the detection and classification of suspicious behavior in HD videos. Finally, an extensive set of experiments are performed and this method is compared with some of the most recent approaches in the field using publicly available datasets as well as a new annotated human action dataset including actions performed in complex scenarios.


Support Vector Machine Motion Vector Video Streaming Input Video Human Activity Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. In: ACM (2011)Google Scholar
  2. 2.
    Rao C., Shah, M.: View-invariance in action recognition. In: CVPR (2001)Google Scholar
  3. 3.
    Savarese, S., Delpozo, A., Niebles, J., Fei-Fei, L.: Spatial-temporal correlations for unsupervised action classification. In: WMVC (2008)Google Scholar
  4. 4.
    Rodriguez, M.D., Ahmed, J., Shah, M.: Action MACH: a spatio-temporal maximum average correlation height filter for action recognition. In: CVPR (2008)Google Scholar
  5. 5.
    Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: ICCV (2009)Google Scholar
  6. 6.
    Jiang, H., Drew, M., Li, Z.: Successive convex matching for action detection. In: CVPR (2006)Google Scholar
  7. 7.
    Veeraraghavan, A., Chellappa, R., Roy-Chowdhury, A.: The function space of an activity. In: CVPR (2006)Google Scholar
  8. 8.
    Natarajan, P., Nevatia, R.: Coupled hidden semi-markov models for activity recognition. In: WMVC (2007)Google Scholar
  9. 9.
    Damen, D., Hogg, D.: Recognizing linked events: searching the space of feasible explanations. In: CVPR (2009)Google Scholar
  10. 10.
    Joo, S.W., Chellappa, R.: Attribute grammar-based event recognition and anomaly detection. In: CVPR (2006)Google Scholar
  11. 11.
    Ryoo, M.S., Aggarwal, J.K.: Semantic representation and recognition of continued and recursive human activities. In: IJCV (2009)Google Scholar
  12. 12.
    Ryoo, M.S., Aggarwal, J.K.: Recognition of composite human activities through context-free grammar based representation. In: CVPR (2006)Google Scholar
  13. 13.
    Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: ICPR (2004)Google Scholar
  14. 14.
    Marszałek, M., Laptev, I., Schmid, C.: Actions in context. In: CVPR (2009)Google Scholar
  15. 15.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: ICCV (2005)Google Scholar
  16. 16.
    Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)Google Scholar
  17. 17.
    Liu, J., Luo, J., Shah, M.: Recognizing realistic actions from videos’in the wild’. In: CVPR (2009)Google Scholar
  18. 18.
    Porikli, F., Bashir, F., Sun, H.: Compressed domain video object segmentation, IEEE Trans. Circuits Syst. Video Technol. 20(1), 2–14 (2010)CrossRefGoogle Scholar
  19. 19.
    Messing, R., Pal, C., Kautz, H.: Activity recognition using the velocity histories of tracked keypoints. In: ICCV (2009)Google Scholar
  20. 20.
    Wang, H., Klaser, A., Schmid, C., Liu, C.L.: Action recognition by dense trajectories. In: CVPR (2011)Google Scholar
  21. 21.
    Sadanand, S., Corso, J.J.: Action Bank: a high-level representation of activity in video. In: CVPR (2012)Google Scholar
  22. 22.
    Lavee, G., Khan, L., Thuraisingham, B.: A framework for a video analysis tool for suspicious event detection. In: MDM (2005)Google Scholar
  23. 23.
    Mecocci, A., Pannozzo, M., Fumarola, A.: Automatic Detection of Anomalous Behavioural Events for Advanced Real-Time Video Surveilance, International Symposium on Computational Intelligence for Measurement Systems and Applications, pp. 187–192. Lugano (2003)Google Scholar
  24. 24.
    Barbará, D., Filippone, M.: Detecting suspicious behavior in surveillance images. In: ICDMW (2008)Google Scholar
  25. 25.
    Wiliem, A., Madasu, V., Boles, W., Yarlagadda, P.: A context-based approach for detecting suspicious behaviours. In: DICTA (2009)Google Scholar
  26. 26.
    Qamar, S.A., Jaffar, M.A., Habib, H.A.: A supervisory system to detect suspicious behavior in online testing system. In: ICMLC (2009)Google Scholar
  27. 27.
    Kaluža, B., Kaminka, G.A., Tambe, M.: Detection of suspicious behavior from a sparse set of multiagent interactions. In: AAMAS (2012)Google Scholar
  28. 28.
    Schindler, K., Gool, L.J.V.: Action snippets: how many frames does human action recognition require?. In: CVPR (2008)Google Scholar
  29. 29.
    Gilbert, A., Illingworth, J., Bowden, R.: Fast realistic multi-action recognition using mined dense spatio-temporal features. In: ICCV (2009)Google Scholar
  30. 30.
    Yao, A., Gall, J., Van Gool, L.: A hough transform-based voting framework for action recognition. In: CVPR (2010)Google Scholar
  31. 31.
    Barnich, Olivier, Van Droogenbroeck, Marc: ViBe: a universal background subtraction algorithm for video sequences. IEEE Trans. Image Process 20(6), 1709–1724 (2011)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Jiangbin, Zheng, Xiuxiu, Li, Yanning, Zhang: Novel tacking algorithm for video surveillance. Syst. Eng. Electron. 29(11), 191–193 (2007)Google Scholar
  33. 33.
    Nagasaka, A., Tanaka, Y.: Automatic video indexing and full video search for object appearances. Visual Database Systems II, pp. 113–127 (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Chundi Mu
    • 1
  • Jianbin Xie
    • 1
  • Wei Yan
    • 1
    Email author
  • Tong Liu
    • 1
  • Peiqin Li
    • 1
  1. 1.College of Electronic Science and EngineeringNational University of Defense TechnologyChangshaChina

Personalised recommendations