Abstract
This work presents a novel approach to human detection based action-recognition in real-time. To realize this goal our method first detects humans in different poses using a correlation-based approach. Recognition of actions is done afterward based on the change of the angular values subtended by various body parts. Real-time human detection and action recognition are very challenging, and most state-of-the-art approaches employ complex feature extraction and classification techniques, which ultimately becomes a handicap for real-time recognition. Our correlation-based method, on the other hand, is computationally efficient and uses very simple gradient-based features. For action recognition angular features of body parts are extracted using a skeleton technique. Results for action recognition are comparable with the present state-of-the-art.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ahmad, M., Lee, S.: Hmm-based human action recognition using multiview image sequences, Hong Kong, China, vol. 1, pp. 263–266 (August 2006)
Ahmad, M., Lee, S.: Human action recognition using multi-view image sequence features. In: 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK, pp. 10–12 (April 2006)
Ali, A., Aggarwal, J.K.: Segmentation and recognition of continuous human activity. Event, 28 (2001)
Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts and shadows in video streams. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1337–1342 (2003)
Cutler, R., Davis, L.: Robust real-time periodic motion detection, analysis, and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 781–796 (2000)
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection, San Diego, CA, USA, vol. 2, pp. 886–893 (June 2005)
Davis, J.W., Bobick, A.F.: The representation and recognition of human movement using temporal templates. IEEE Transactions on Computer Vision and Pattern Recognition, 928–934 (June 1997)
Gavrila, D.M., Giebel, J.: Shape-based pedestrian detection and localization. In: IEEE Intelligent Vehicle Symposium, Versailles, France, vol. 1, pp. 8–14 (June 2002)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 2247–2253 (2007)
Guo, Y., Xu, G., Tsuji, S.: Understanding human motion patterns. In: 12th IAPR Conference on Computer Vision and Image Processing, Jerusalem, Israel, pp. 325–329 (1994)
Jiang, L., Tian, F., Shen, L.E., Yao, S., Lu, Z., Xu, L.: Perceptual-based fusion of ir and visual images for human detection. In: International Symposium on Intelligent Multimedia, Video and Speech Processing, pp. 514–517 (2004)
Jones, M., Viola, P.: Detecting pedestrians using patterns of motion and appearance. In: IEEE International Conference on Computer Vision, Nice, France, pp. 734–741 (2003)
Masoud, O., Papanikolopoulos, N.P.: A method for human action recognition, vol. 21, pp. 729–743 (August 2003)
Mendoza, M.A., Pérez de la Blanca, N.: Hmm-based action recognition using contour histograms. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 394–401. Springer, Heidelberg (2007)
Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Computer Vision and Image Understanding 8, 231–268 (2006)
Ogale, N.A.: A survey of techniques for human detection from video, USA, Department of Computer Science, University Of Maryland, College Park
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: International Conference on Pattern Recognition, Cambridge, UK, pp. 32–36 (August 2004)
Sundaresan, A., RoyChowdhury, A., Chellappa, R.: A hidden markov model based framework for recognition of humans from gait sequences. In: International Conference on Image Processing, Barcelona, Catalunia, Spain, pp. 93–96 (September 2003)
Wren, C., Azarbayejani, A., Darrell, T., Pentl, A.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)
Yoon, S.M., Kim, H.: Real-time multiple people detection using skin color, motion and appearence information. In: International Workshop on Robot and Human Interactive Communication, Kurashiki, Okayama Japan, pp. 331–334 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chakraborty, B., Bagdanov, A.D., Gonzàlez, J. (2009). Towards Real-Time Human Action Recognition. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2009. Lecture Notes in Computer Science, vol 5524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02172-5_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-02172-5_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02171-8
Online ISBN: 978-3-642-02172-5
eBook Packages: Computer ScienceComputer Science (R0)