Motion Binary Patterns for Action Recognition

Florian Baumann, Jie Lao, Arne Ehlers, Bodo Rosenhahn

2014

Abstract

In this paper, we propose a novel feature type to recognize human actions from video data. By combining the benefit of Volume Local Binary Patterns and Optical Flow, a simple and efficient descriptor is constructed. Motion Binary Patterns (MBP) are computed in spatio-temporal domain while static object appearances as well as motion information are gathered. Histograms are used to learn a Random Forest classifier which is applied to the task of human action recognition. The proposed framework is evaluated on the well-known, publicly available KTH dataset, Weizman dataset and on the IXMAS dataset for multi-view action recognition. The results demonstrate state-of-the-art accuracies in comparison to other methods.

References

  1. Aggarwal, J. and Ryoo, M. (2011). Human activity analysis: A review. ACM Computing Surveys, 43(3):16:1- 16:43.
  2. Amit, Y. and Geman, D. (1997). Shape quantization and recognition with randomized trees. Neural Computation, 9(7):1545-1588.
  3. Blank, M., Gorelick, L., Shechtman, E., Irani, M., and Basri, R. (2005). Actions as space-time shapes. In Computer Vision (ICCV), 10th International Conference on, pages 1395-1402.
  4. Breiman, L. (1996). Bagging predictors. In Machine Learning, volume 24, pages 123-140.
  5. Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.
  6. Fehr, J. (2007). Rotational invariant uniform local binary patterns for full 3d volume texture analysis. In Finnish signal processing symposium (FINSIG).
  7. Gorelick, L., Blank, M., Shechtman, E., Irani, M., and Basri, R. (2007). Actions as space-time shapes. Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on, 29(12):2247-2253.
  8. Ho, T. K. (1995). Random decision forests. In Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on, volume 1, pages 278-282. IEEE.
  9. Ho, T. K. (1998). The random subspace method for constructing decision forests. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(8):832- 844.
  10. Horn, B. K. and Schunck, B. G. (1981). Determining optical flow. Artificial Intelligence, 17.
  11. Liu, J., Luo, J., and Shah, M. (2009). Recognizing realistic actions from videos “in the wild”. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 1996-2003. IEEE.
  12. Lui, Y. M., Beveridge, J., and Kirby, M. (2010). Action classification on product manifolds. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  13. Marszalek, M., Laptev, I., and Schmid, C. (2009). Actions in context. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  14. Mattivi, R. and Shao, L. (2009). Human action recognition using lbp-top as sparse spatio-temporal feature descriptor. In Computer Analysis of Images and Patterns (CAIP).
  15. O'Hara, S. and Draper, B. (2012). Scalable action recognition with a subspace forest. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  16. Ojala, T., Pietikainen, M., and Harwood, D. (1994). Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In Pattern Recognition. Proceedings of the 12th IAPR International Conference on.
  17. Poppe, R. (2010). A survey on vision-based human action recognition. Image and Vision Computing, 28(6):976 - 990.
  18. Schindler, K. and Van Gool, L. (2008). Action snippets: How many frames does human action recognition require? In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  19. Schuldt, C., Laptev, I., and Caputo, B. (2004). Recognizing human actions: a local svm approach. In Pattern Recognition. (ICPR). Proceedings of the 17th International Conference on.
  20. Shao, L. and Mattivi, R. (2010). Feature detector and descriptor evaluation in human action recognition. In Proceedings of the ACM International Conference on Image and Video Retrieval.
  21. Tian, Y., Sukthankar, R., and Shah, M. (2013). Spatiotemporal deformable part models for action detection. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  22. Topi, M., Timo, O., Matti, P., and Maricor, S. (2000). Robust texture classification by subsets of local binary patterns. In Pattern Recognition. (ICPR). Proceedings of the 15th International Conference on.
  23. Wang, Z., Wang, J., Xiao, J., Lin, K.-H., and Huang, T. (2012). Substructure and boundary modeling for continuous action recognition. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  24. Weinland, D., O zuysal, M., and Fua, P. (2010). Making action recognition robust to occlusions and viewpoint changes,. In European Conference on Computer Vision (ECCV).
  25. Weinland, D., Ronfard, R., and Boyer, E. (2006). Free viewpoint action recognition using motion history volumes. In Computer Vision and Image Understanding (CVIU).
  26. Wu, X., Xu, D., Duan, L., and Luo, J. (2011). Action recognition using context and appearance distribution features. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  27. Yeffet, L. and Wolf, L. (2009). Local trinary patterns for human action recognition. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on.
  28. Yu, T.-H., Kim, T.-K., and Cipolla, R. (2013). Unconstrained monocular 3d human pose estimation by action detection and cross-modality regression forest. In Computer Vision and Pattern Recognition, (CVPR). IEEE Conference on, pages 3642-3649.
  29. Zhao, G. and Pietikainen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on, 29(6):915-928.
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Paper Citation


in Harvard Style

Baumann F., Lao J., Ehlers A. and Rosenhahn B. (2014). Motion Binary Patterns for Action Recognition . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 385-392. DOI: 10.5220/0004816903850392


in Bibtex Style

@conference{icpram14,
author={Florian Baumann and Jie Lao and Arne Ehlers and Bodo Rosenhahn},
title={Motion Binary Patterns for Action Recognition},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={385-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004816903850392},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Motion Binary Patterns for Action Recognition
SN - 978-989-758-018-5
AU - Baumann F.
AU - Lao J.
AU - Ehlers A.
AU - Rosenhahn B.
PY - 2014
SP - 385
EP - 392
DO - 10.5220/0004816903850392