Abstract
Action recognition in video and still image is one of the most challenging research topics in pattern recognition and computer vision. This paper proposes a new method for video action classification based on 3D Zernike moments. These last ones aim to capturing both structural and temporal information of a time varying sequence. The originality of this approach consists to represent actions in video sequences by a three-dimension shape obtained from different silhouettes in the space-time volume. In fact, the given video is segmented in space-time volume. Then, silhouettes are extracted from obtained images of the video sequences volumes and 3D Zernike moments are computed for video, based on silhouettes volumes. Finally, least square version of SVM (LSSVM) classifier with extracted features is used to classify actions in videos. To evaluate the proposed approach, it was applied on a benchmark human action dataset. The experimentations and evaluations show efficient results in terms of action characterizations and classification. Further more, it presents several advantages such as simplicity and respect of silhouette movement progress in the video guaranteed by 3D Zernike moment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zelnik-Manor, L., Irani, M.: Event-Based Analysis of Video, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition. In: Proceedings of the 2001 IEEE Computer Society Conference, vol. 2, pp. 123–130 (2001)
Wu, X., Ngo, C.W., Hauptmann, A.G., Tan, H.K.: “Real-Time Near-Duplicate Elimination for Web Video Search with Content and Context. IEEE Transaction on Multimedia 11, 196–207 (2009)
Efros, A., Breg, C., Mori, G., Malik, J.: Recognizing Action at a Distance. In: Computer Vision Proceedings. Ninth IEEE International Conference, vol. 2, pp. 726–733 (2003)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72 (2005)
Gavrila, D.: The visual analysis of human movement: A survey. Computer Vision Image Understand. 73, 82–98 (1999)
Cedras, C., Shah, M.: Motion-based recognition: A survey. Image Vision Computer 13, 129–155 (1995)
Rao, C., Shah, M.: View-invariance in action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 316–321 (2001)
Song, Y., Goncalves, L., Perona, P.: Unsupervised learning of human motion. IEEE Transaction on Pattern Analyses and Machine Intelligence 25, 814–827 (2003)
Yacoob, Y., Black, M.: Parameterized modeling and recognition of activities. Computer Vision on Image Understand. 73, 232–247 (1999)
Ali, A., Aggarwal, J.: Segmentation and recognition of continuous human activity. In: Procedure of Intelligent Workshop on Detection and Recognition of Events in Video, pp. 28–35 (2001)
Bobick, A., Davis, J.: The recognition of human movement using temporal templates. IEEE Transaction on Pattern Analyses and Machine Intelligence 23, 257–267 (2001)
Weinland, D., Ronfard, R., Boyer, E.: Motion history volumes for free viewpoint action recognition. Presented at the IEEE Workshop Modeling People and Human Interaction, pp. 87–89 (2005)
Gorelick, L.: al Actions as Space-Time Shapes. IEEE Transactions Pattern Analysis and Machine Intelligence 29, 2247–2253 (2007)
Guo, K., Ishwar, P., Konrad, J.: Action Recognition in Video by Covariance Matching of Silhouette Tunnels. In: XXII Brazilian Symposium on Computer Graphics and Image Processing, pp. 299–306 (2009)
Kellokumpu, V., Pietikainen, M., Heikkila, J.: Human activity recognition using sequences of postures. In: IAPR Conference on Machine Vision Applications, p. 6570–6573 (2005)
Sminchisescu, C., Kanaujia, A., Li, Z., Metaxas, D.: Conditional models for contextual human motion recognition. In: Procedure International of Conference on Computer Vision, vol. 2, pp. 1808–1815 (2005)
Shutler, J.D., et al.: Statistical gait recognition via temporal moments. In: Image Analysis and Interpretation 4th IEEE Southwest Symposium, pp. 291–295 (2000)
Baccouche1, M., Mamalet, F., Wolf, C., Garcia, C., Baskurt, A.: Une approche neuronale pour la classification d’actions de sport par la prise en compte du contenu visuel et du mouvement dominant. In: Compression et Représentation des Signaux Audiovisuels (CORESA), pp. 25–30 (2010)
Tian, Y., Liu, Z., Yao, B., Zhang, Z., Huang, T.: Action Detection Using Multiple Spatial-Temporal Interest Point Features. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 340–345 (2010)
Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machines. Neural Processing Letters 9(3), 293–300 (2002)
Gestel, T.V., Suykens, A.K.: Benchmarking Least Squares Support Vector Machine Classifiers, Technical report of Internal Report 00-37 on ESAT-SISTA 54, 5–32 (2000)
Blank, M., Gorelick, L., Shechtman, M., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Transactions on pattern Analysis and Machine Intelligence 29, 2247–2253 (2005)
Green, R., Guan, L.: Quantifying and recognizing human movement patterns from monocular video images. IEEE Transaction Circuits on System Video Technologys 14, 179–190 (2004)
Dhillon, C.H., Nowozin, P.S., Lampert, S.: Combining appearance and motion for human action classification in videos. In: Computer Vision and Pattern Recognition Workshops, pp. 22 – 29 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lassoued, I., Zagrouba, E., Chahir, Y. (2011). An Efficient Approach for Video Action Classification Based on 3D Zernike Moments. In: Park, J.J., Yang, L.T., Lee, C. (eds) Future Information Technology. Communications in Computer and Information Science, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22309-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-22309-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22308-2
Online ISBN: 978-3-642-22309-9
eBook Packages: Computer ScienceComputer Science (R0)