Abstract
We propose a compact representation for human action recognition by employing human block-based model and local optical flow features. The contour descriptor based on block-based model proposed in this paper can be used to represent the movement of human body exactly by dividing silhouette into different blocks. Meanwhile, we propose an accurate and stable optical flow descriptor for motion information. Finally, our action feature vector is constructed by using contour and optical flow descriptor together with global velocity information. In addition, we adopt the well-known Bag-of-words (BoW) model to obtain the final video level representations. Our approach is tested on two public datasets: Weizmann and KTH. Experimental results show that our approach achieves a 100 % test accuracy on Weizmann dataset and outperforms some state-of-the-art techniques on KTH dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Folgado E, Rincón M, Carmona EJ et al (2011) A block-based model for monitoring of human activity. Neurocomputing 74(8):1283–1289
Chaudhry R et al (2009) Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: IEEE conference on computer vision and pattern recognition CVPR, June 2009
Laptev I (2005) On space-time interest points. Int J Comput Vis 64(2-3):107–123
Blank M et al (2005) Actions as space-time shapes. In: 10th IEEE international conference on computer vision ICCV, vol 2
Dollár P et al (2005) Behavior recognition via sparse spatio-temporal features. In: 2nd Joint IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, October 2005, pp 65–72
Niebles JC, Wang H, Fei–Fei L et al (2006) Unsupervised learning of human action categories using spatial-temporal words. In: British machine vision conference BMVC, 2006
Maji SB, Berg AC, Malik J (2008) Classification using intersection kernel support vector machines is efficient. In: IEEE conference on computer vision and pattern recognition, 2008, pp 1–8
Du T, Sorokin A (2008) Human activity recognition with metric learning. In: European conference on computer vision, 2008, pp 548–561
Dollar P, Rabaud V, Cottrell G, Belongie S (2005) Behavior recognition via sparse spatio-temporal features. In: IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, 2005, pp 1–8
Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: IEEE conference on computer vision and pattern recognition, 2009
Ikizler N, Cinbis RG, Duygulu P (2008) Human action recognition with line and flow histograms. In: 19th IEEE international conference on pattern recognition, ICPR, 2008, pp 1–4
Jhuang H, Serre T, Wolf L, Poggio T (2007) A biologically inspired system for action recognition. In: 11th IEEE international conference on computer vision ICCV, 2007
Wong S-F, Kim T-K, Cipolla R (2007) Learning motion categories using both semantic and structural information. In: IEEE conference on computer vision and pattern recognition (CVPR 2007), Minneapolis, USA, June 2007
Acknowledgments
This work is funded by National Science Foundation of China (61175009).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Song, J., Hu, F. (2014). Human Action Recognition with Block-Based Model and Flow Histograms. In: Farag, A., Yang, J., Jiao, F. (eds) Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013). Lecture Notes in Electrical Engineering, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41407-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-41407-7_40
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41406-0
Online ISBN: 978-3-642-41407-7
eBook Packages: EngineeringEngineering (R0)