International Conference on Mining Intelligence and Knowledge Exploration

Mining Intelligence and Knowledge Exploration pp 460-471 | Cite as

Vision-Based Human Action Recognition in Surveillance Videos Using Motion Projection Profile Features

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)

Abstract

Human Action Recognition (HAR) is a dynamic research area in pattern recognition and artificial Intelligence. The area of human action recognition consistently focuses on changes in the scene of a subject with reference to time, since motion information can prudently depict the action. This paper depicts a novel framework for action recognition based on Motion Projection Profile (MPP) features of the difference image, representing various levels of a person’s posture. The motion projection profile features consist of the measure of moving pixel of each row, column and diagonal (left and right) of the difference image and gives adequate motion information to recognize the instantaneous posture of the person. The experiments are carried out using WEIZMANN and AUCSE datasets and the extracted features are modeled by the GMM classifier for recognizing human actions. In the experimental results, GMM exhibit effectiveness of the proposed method with an overall accuracy rate of 94.30 % for WEIZMANN dataset and 92.49 % for AUCSE dataset.

Keywords

Video surveillance Human action recognition Frame difference Feature extraction Gaussian mixture models 

References

  1. 1.
    Vishwakarma, S., Agrawal, A.: A survey on activity recognition and behavior understanding in video surveillance. Vis. Comput. 29(10), 983–1009 (2013)CrossRefGoogle Scholar
  2. 2.
    Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. Comput. Vis. Image Underst. 115(2), 224–241 (2011)CrossRefGoogle Scholar
  3. 3.
    Hassan, M., Ahmad, T., Liaqat, N., Farooq, A., Ali, S.A., et al.: A review on human actions recognition using vision based techniques. J. Image Graph. 2(1), 28–32 (2014)CrossRefGoogle Scholar
  4. 4.
    Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010)CrossRefGoogle Scholar
  5. 5.
    Duda, R.O., Hart, P.E., et al.: Pattern Classification and Scene Analysis, vol. 3. Wiley, New York (1973)MATHGoogle Scholar
  6. 6.
    Gonzalez, R.C.: Digital Image Processing. Pearson Education, India (2009)Google Scholar
  7. 7.
    Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)CrossRefGoogle Scholar
  8. 8.
    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. Pattern Anal. Mach. Intell. 23(3), 257–267 (2001)CrossRefGoogle Scholar
  9. 9.
    Wang, H., Kläser, A., Schmid, C., Liu, C.-L.: Action recognition by dense trajectories. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3169–3176. IEEE (2011)Google Scholar
  10. 10.
    Mu, C., Xie, J., Yan, W., Liu, T., Li, P.: A fast recognition algorithm for suspicious behavior in high definition videos. Multimedia Syst., 1–11 (2015)Google Scholar
  11. 11.
    Iglesias-Ham, M., García-Reyes, E.B., Kropatsch, W.G., Artner, N.M.: Convex deficiencies for human action recognition. J. Intell. Robot. Syst. 64(3–4), 353–364 (2011)CrossRefGoogle Scholar
  12. 12.
    Vezzani, R., Baltieri, D., Cucchiara, R.: HMM based action recognition with projection histogram features. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 286–293. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  13. 13.
    Arunnehru, J., Geetha, M.K.: Automatic activity recognition for video surveillance. Int. J. Comput. Appl. 75(9), 1–6 (2013)Google Scholar
  14. 14.
    Arunnehru, J., Geetha, M.K.: Human activity recognition based on projected histogram features in surveillance videos using tree based classifiers. Int. J. Appl. Eng. Res. 9(21), 4950–4954 (2014)Google Scholar
  15. 15.
    McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, New York (2004) Google Scholar
  16. 16.
    Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: Tenth IEEE International Conference on Computer Vision, 2005, ICCV 2005, vol. 2, pp. 1395–1402. IEEE (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Speech and Vision Lab, Department of Computer Science and EngineeringAnnamalai UniversityAnnamalainagarIndia

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