Advertisement

A Better Trajectory Shape Descriptor for Human Activity Recognition

  • Pejman HabashiEmail author
  • Boubakeur Boufama
  • Imran Shafiq Ahmad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10317)

Abstract

Sparse representation is one of the most popular methods for human activity recognition. Sparse representation describes a video by a set of independent descriptors. Each of these descriptors usually captures the local information of the video. These features are then mapped to another space, using Fisher Vectors, and an SVM is used for clustering them. One of the sparse representation methods proposed in the literature uses trajectories as features. Trajectories have been shown to be discriminative in many previous works on human activity recognition. In this paper, a more formal definition is given to trajectories and a new more effective trajectory shape descriptor is proposed. We tested the proposed method against our challenging dataset and demonstrated through experiments that our new trajectory descriptor outperforms the previously existing main shape descriptor with a good margin. For example, in one case the obtained results had a 5.58% improvement, compared to the existing trajectory shape descriptor. We run our tests over sparse feature sets, and we are able to reach comparable results to a dense sampling method, with fewer computations.

Keywords

Human activity recognition Trajectory descriptor Trajectory encoding Shape descriptor Shape encoding 

References

  1. 1.
    Wang, H., Klaser, 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
  2. 2.
    Habashi, P., Boufama, B., Ahmad, I.S.: The bag of micro-movements for human activity recognition. In: Kamel, M., Campilho, A. (eds.) ICIAR 2015. LNCS, vol. 9164, pp. 269–276. Springer, Cham (2015). doi: 10.1007/978-3-319-20801-5_29 CrossRefGoogle Scholar
  3. 3.
    Mohammadi, E., Wu, Q.J., Saif, M.: Human action recognition by fusing the outputs of individual classifiers. In: 2016 13th Conference on Computer and Robot Vision (CRV), pp. 335–341. IEEE (2016)Google Scholar
  4. 4.
    Wang, Y., Tran, V., Hoai, M.: Evolution-preserving dense trajectory descriptors. arXiv preprint arXiv:1702.04037 (2017)
  5. 5.
    Wang, H., Oneata, D., Verbeek, J., Schmid, C.: A robust and efficient video representation for action recognition. Int. J. Comput. Vis. 119(3), 219–238 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Wang, H., Schmid, C.: Action recognition with improved trajectories. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3551–3558. IEEE (2013)Google Scholar
  7. 7.
    Chang, C.-C. Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
  8. 8.
    Laptev, I., Lindeberg, T.: Interest point detection and scale selection in space-time. In: Griffin, L.D., Lillholm, M. (eds.) Scale-Space 2003. LNCS, vol. 2695, pp. 372–387. Springer, Heidelberg (2003). doi: 10.1007/3-540-44935-3_26 CrossRefGoogle Scholar
  9. 9.
    Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)CrossRefGoogle Scholar
  10. 10.
    Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72. IEEE (2005)Google Scholar
  11. 11.
    Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of the 15th international conference on Multimedia, pp. 357–360. ACM (2007)Google Scholar
  12. 12.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). doi: 10.1007/11744023_34 CrossRefGoogle Scholar
  13. 13.
    Rosten, E., Porter, R., Drummond, T.: Faster and better: a machine learning approach to corner detection. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 105–119 (2010)CrossRefGoogle Scholar
  14. 14.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  15. 15.
    Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428–441. Springer, Heidelberg (2006). doi: 10.1007/11744047_33 CrossRefGoogle Scholar
  16. 16.
    Wang, H., Kläser, A., Schmid, C., Liu, C.-L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Mohammadi, E., Wu, Q.J., Saif, M.: Human activity recognition using an ensemble of support vector machines. In: 2016 International Conference on High Performance Computing and Simulation (HPCS), pp. 549–554. IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pejman Habashi
    • 1
    Email author
  • Boubakeur Boufama
    • 1
  • Imran Shafiq Ahmad
    • 1
  1. 1.School of Computer ScienceUniversity of WindsorWindsorCanada

Personalised recommendations