Anomaly Detection over Spatiotemporal Object Using Adaptive Piecewise Model

  • Fazli Hanapiah
  • Ahmed A. Al-Obaidi
  • Chee Seng Chan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6230)


Motion trajectories provide rich spatio-temporal information about an object activity. In this paper, we present a novel anomaly detection framework to detect anomalous motion trajectory using the fusion of adaptive piecewise analysis and fuzzy rule-based method. That is, first of all we address the problem by segmenting our moving objects using a Gaussian mixture background model. Secondly, visual tracking using probabilistic appearance manifolds to extract spatio-temporal trajectory. Thirdly, adaptive piecewise analysis and data quantization are performed on the extracted trajectory such that the anomalous detection can be performed as the incoming data are acquired. Finally, through the accumulative rank of the adaptive piecewise analysis and a fuzzy rule-based anomaly detection framework to detect the anomalous trajectory. Experimental results on various challenging trajectory data has validated the effectiveness of the proposed method.


Fuzzy Rule Action Recognition Anomaly Detection Visual Tracking Output Membership Function 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Brand, M., Oliver, N., Pentland, A.: Coupled hidden markov models for complex action recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 994–1002 (1997)Google Scholar
  2. 2.
    Chan, C.S., Liu, H.: Fuzzy qualitative human motion analysis. IEEE Transactions on Fuzzy Systems 17(4), 851–862 (2009)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Cuntoor, N., Yegnanarayana, B., Chellappa, R.: Activity modeling using event probability sequences. IEEE Transactions on Image Processing 17(4), 594–607 (2008)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Dimitrios, M., Ellis, T.: Path detection in video surveillance. Image and Vision Computing 20(12) (2002)Google Scholar
  5. 5.
    Duong, T., Bui, H., Phung, D., Venkatesh, S.: Activity recognition and abnormality detection with the switching hidden semi-markov model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 838–845 (June 2005)Google Scholar
  6. 6.
    Grimson, W., Stauffer, C., Romano, R., Lee, L.: Using adaptive tracking to classify and monitor activities in a site. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 22–28. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  7. 7.
    Hongeng, S., Nevatia, R.: Multi-agent event recognition. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, vol. 2, pp. 84–91 (2001)Google Scholar
  8. 8.
    Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(9), 1450–1464 (2006)CrossRefGoogle Scholar
  9. 9.
    Lee, K.-C., Ho, J., Yang, M.-H., Kriegman, D.: Visual tracking and recognition using propabilistic appearance manifolds. Computer Vision and Image Understanding 99(3), 303–331 (2005)CrossRefGoogle Scholar
  10. 10.
    Lemire, D.: A better alternative to piecewise linear time series segmentation. In: Proceedings of the SIAM International Conference on Data Mining (April 2007)Google Scholar
  11. 11.
    Medioni, G., Cohen, I., Brmond, F., Hongeng, S., Nevatia, R.: Event detection and analysis from video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(8), 873–889 (2001)CrossRefGoogle Scholar
  12. 12.
    Niu, W., Long, J., Han, D., Wang, Y.-F.: Human activity detection and recognition for video surveillance. In: IEEE International Conference on Multimedia and Expo., vol. 1, pp. 719–722 (June 2004)Google Scholar
  13. 13.
    Oliver, N., Garg, A., Horvitz, E.: Layered representations for learning and inferring office activity from multiple sensory channels. Computer Vision and Image Understanding 96(2), 163–180 (2004)CrossRefGoogle Scholar
  14. 14.
    Park, S., Aggarwal, J.: A hierarchical bayesian network for event recognition of human actions and interactions. Multimedia Systems 10(2), 164–179 (2004)CrossRefGoogle Scholar
  15. 15.
    Vitaladevuni, S., Kellokumpu, V., Davis, L.: Action recognition using ballistic dynamics. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (June 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fazli Hanapiah
    • 1
  • Ahmed A. Al-Obaidi
    • 1
  • Chee Seng Chan
    • 1
  1. 1.Centre of Multimodal Signal Processing, Mimos BerhadTechnology Park MalaysiaKuala LumpurMalaysia

Personalised recommendations