HUMAN ACTION RECOGNITION USING DIRECTION AND MAGNITUDE MODELS OF MOTION

Yassine Benabbas, Samir Amir, Adel Lablack, Chabane Djeraba

2011

Abstract

This paper proposes an approach that uses direction and magnitude models to perform human action recognition from videos captured using monocular cameras. A mixture distribution is computed over the motion orientations and magnitudes of optical flow vectors at each spatial location of the video sequence. This mixture is estimated using an online k-means clustering algorithm. Thus, a sequence model which is composed of a direction model and a magnitude model is created by circular and non-circular clustering. Human actions are recognized via a metric based on the Bhattacharyya distance that compares the model of a query sequence with the models created from the training sequences. The proposed approach is validated using two public datasets in both indoor and outdoor environments with low and high resolution videos.

References

  1. Ali, S. and Shah, M. (2010). Human action recognition in videos using kinematic features and multipleinstance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 32(2):288-303.
  2. Bouguet, J.-Y. (2000). Pyramidal implementation of the lucas kanade feature tracker description of the algorithm. Intel Corporation Microprocessor Research Labs.
  3. Djeraba, C., Lablack, A., and Benabbas, Y. (2010). MultiModal User Interactions in Controlled Environments. Springer-Verlag.
  4. Dollar, P., Rabaud, V., Cottrell, G., and Belongie, S. (2005). Behavior recognition via sparse spatio-temporal features. In 2nd International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (PETS), pages 65-72.
  5. Escobar, M.-J., Masson, G. S., Vieville, T., and Kornprobst, P. (2009). Action recognition using a bio-inspired feedforward spiking network. International Journal of Computer Vision (IJCV), 82(3):284-301.
  6. Fathi, A. and Mori, G. (2008). Action recognition by learning mid-level motion features. In International Conference on Computer Vision and Pattern Recognition (CVPR).
  7. Ganesh, S. and Bajcsy, R. (2008). Recognition of human actions using an optimal control based motor model. In Workshop on Applications of Computer Vision (WACV).
  8. Turaga, P., Chellappa, R., Subrahmanian, V. S., and Udrea, O. (2008). Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 18(11):1473-1488.
  9. Wang, H., Ullah, M. M., Kläser, A., Laptev, I., and Schmid, C. (2009a). Evaluation of local spatio-temporal features for action recognition. In British Machine Vision Conference.
  10. Wang, L., Zhou, H., Low, S.-C., and Leckie, C. (2009b). Action recognition via multi-feature fusion and gaussian process classification. In Workshop on Applications of Computer Vision (WACV), pages 1 -6.
  11. Willems, G., Tuytelaars, T., , and Gool, L. V. (2008). An efficient dense and scale-invariant spatio-temporal interest point detector. In European Conference on Computer Vision (ECCV).
  12. Yang, W., Wang, Y., and Mori, G. (2009). Efficient human action detection using a transferable distance function. In Asian Conference on Computer Vision (ACCV).
  13. Zhang, J. and Gong, S. (2010). Action categorization with modified hidden conditional random field. Pattern Recognition (PR), 43(1):197-203.
Download


Paper Citation


in Harvard Style

Benabbas Y., Amir S., Lablack A. and Djeraba C. (2011). HUMAN ACTION RECOGNITION USING DIRECTION AND MAGNITUDE MODELS OF MOTION . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 277-285. DOI: 10.5220/0003323702770285


in Bibtex Style

@conference{visapp11,
author={Yassine Benabbas and Samir Amir and Adel Lablack and Chabane Djeraba},
title={HUMAN ACTION RECOGNITION USING DIRECTION AND MAGNITUDE MODELS OF MOTION},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={277-285},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003323702770285},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - HUMAN ACTION RECOGNITION USING DIRECTION AND MAGNITUDE MODELS OF MOTION
SN - 978-989-8425-47-8
AU - Benabbas Y.
AU - Amir S.
AU - Lablack A.
AU - Djeraba C.
PY - 2011
SP - 277
EP - 285
DO - 10.5220/0003323702770285