Abstract
Since video recording devices have become ubiquitous, the automated analysis of human activity from a single uncalibrated video has become an essential area of research in visual surveillance. Despite variability in terms of human appearance and motion styles, in the last couple of years, a few computer vision systems have reported very encouraging results. Would these methods be already suitable for visual surveillance applications? Alas, few of them have been evaluated in the two most challenging scenarios for an action recognition system: view independence and human interactions. Here, first a review of monocular human action recognition methods that could be suitable for visual surveillance is presented. Then, the most promising frameworks, i.e. methods based on advanced dimensionality reduction, bag of words and random forest, are described and evaluated on IXMAS and UT-Interaction datasets. Finally, suitability of these systems for visual surveillance applications is discussed.
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
Preview
Unable to display preview. Download preview PDF.
References
Blackburn, J., Ribeiro, E.: Human motion recognition using isomap and dynamic time warping. In: Elgammal, A., Rosenhahn, B., Klette, R. (eds.) Human Motion 2007. LNCS, vol. 4814, pp. 285–298. Springer, Heidelberg (2007)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Chin, T., Wang, L., Schindler, K., Suter, D.: Extrapolating learned manifolds for human activity recognition. In: ICIP 2007 (2007)
Cheng, Z., Qin, L., Huang, Q., Jiang, S., Tian, Q.: Group Activity Recognition by Gaussian Processes Estimation. In: ICPR 2010 (2010)
Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision at ECCV 2004, pp. 1–22 (2004)
Fang, C.-H., Chen, J.-C., Tseng, C.-C., Lien, J.-J.J.: Human action recognition using spatio-temporal classification. In: Zha, H., Taniguchi, R.-i., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5995, pp. 98–109. Springer, Heidelberg (2010)
Gilbert, A., Illingworth, J., Bowden, R.: Fast Realistic Multi-Action Recognition using Mined Dense Spatio-temporal Features. In: ICCV 2009 (2009)
Gorelick, L., Galun, M., Sharon, E., Basri, R., Brandt, A.: Shape representation and classification using the poisson equation. PAMI 28(12), 1991–2005 (2006)
Hu, Y., Cao, L., Lv, F., Yan, S., Gong, Y., Huang, T.S.: Action Detection in Complex Scenes with Spatial and Temporal Ambiguities. In: ICCV 2009 (2009)
Jia, K., Yeung, D.: Human action recognition using local spatio-temporal discriminant embedding. In: CVPR 2008 (2008)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features In: ECML 1998 (1998)
Junejo, I.N., Dexter, E., Laptev, I., Pérez, P.: Cross-view action recognition from temporal self-similarities. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 293–306. Springer, Heidelberg (2008)
Kaaniche, M.B., Bremond, F.: Gesture Recognition by Learning Local Motion Signatures. In: CVPR 2010 (2010)
Kovashka, A., Grauman, K.: Learning a Hierarchy of Discriminative Space-Time Neighborhood Features for Human Action Recognition. In: CVPR 2010 (2010)
The KTH Database, http://www.nada.kth.se/cvap/actions/
Laptev, I.: On Space-Time Interest Points. International Journal of Computer Vision 64(2/3), 107–123 (2005)
Laptev, I., Perez, P.: Retrieving Actions in Movies. In: ICCV 2007 (2007)
Lewandowski, M., Makris, D., Nebel, J.-C.: View and style-independent action manifolds for human activity recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 547–560. Springer, Heidelberg (2010)
Lewandowski, M., Martinez, J., Makris, D., Nebel, J.-C.: Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series. In: ICPR 2010 (2010)
Liu, J., Ali, S., Shah, M.: Recognizing human actions using multiple features. In: CVPR 2008 (2008)
Natarajan, P., Singh, V.K., Nevatia, R.: Learning 3D Action Models from a few 2D videos for View Invariant Action Recognition. In: CVPR 2010 (2010)
Niebles, J.C., Chen, C.-W., Fei-Fei, L.: Modeling temporal structure of decomposable motion segments for activity classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 392–405. Springer, Heidelberg (2010)
Orrite, C., Martinez, F., Herrero, E., Ragheb, H., Velastin, S.A.: Independent viewpoint silhouette-based human action modeling and recognition. In: MLVMA 2008 (2008)
Qu, H., Wang, L., Leckie, C.: Action Recognition Using Space-Time Shape Difference Images. In: ICPR 2010 (2010)
Rabiner, L., Juang, B.-H.: Fundamentals of Speech Recognition. Prentice-Hall, Inc., Englewood Cliffs (1993)
Richard, S., Kyle, P.: Viewpoint manifolds for action recognition. EURASIP Journal on Image and Video Processing (2009)
Ryoo, M.S., Aggarwal, J.K.: Spatio-Temporal Relationship Match: Video Structure Comparison for Recognition of Complex Human Activities. In: ICCV 2009 (2009)
Satkin, S., Hebert, M.: Modeling the temporal extent of actions. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 536–548. Springer, Heidelberg (2010)
Thi, T.H., Zhang, J.: Human Action Recognition and Localization in Video using Structured Learning of Local Space-Time Features. In: AVSS 2010 (2010)
Turaga, P., Veeraraghavan, A., Chellappa, R.: Statistical analysis on stiefel and grassmann manifolds with applications in computer vision. In: CVPR 2008, pp. 1–8 (2008)
Wang, L., Suter, D.: Visual learning and recognition of sequential data manifolds with applications to human movement analysis. Computer Vision and Image Understanding 110(2), 153–172 (2008)
Waltisberg, D., Yao, A., Gall, J., Van Gool, L.: Variations of a hough-voting action recognition system. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 306–312. Springer, Heidelberg (2010)
Weinland, D., Ronfard, R., Boyer, E.: Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding 104(2-3), 249–257 (2006)
Weinland, D., Boyer, E., Ronfard, R.: Action recognition from arbitrary views using 3d exemplars. In: ICCV 2007 (2007)
Weinland, D., Özuysal, M., Fua, P.: Making Action Recognition Robust to Occlusions and Viewpoint Changes. In: ECCV 2010 (2010)
The Weizzman Database, http://www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html
Yan, P., Khan, S., Shah, M.: Learning 4D action feature models for arbitrary view action recognition. In: CVPR 2008 (2008)
Yao, A., Gall, J., Van Gool, L.: A Hough Transform-Based Voting Framework for Action Recognition. In: CVPR 2010 (2010)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. NIPS 14, 585–591 (2001)
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
Nebel, JC., Lewandowski, M., Thévenon, J., Martínez, F., Velastin, S. (2011). Are Current Monocular Computer Vision Systems for Human Action Recognition Suitable for Visual Surveillance Applications?. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24031-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-24031-7_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24030-0
Online ISBN: 978-3-642-24031-7
eBook Packages: Computer ScienceComputer Science (R0)