Abstract
A common approach to characterize and recognize human actions consists of detecting interest points within the video volume and describe them locally, followed by a bag-of-words representation. Many of the proposed descriptors are based on the local optic flow, but they may simply summarize the flow in terms of histograms of its orientations. However, potentially interesting and discriminative properties of the optic flow are arguably ignored this way. This work addresses this issue by exploring two new optic flow-based descriptors. One of them consists of kinematic features of spatial variations of the optic flow. The other one captures dynamic patterns of the optic flow in terms of its temporal recurrences. It is experimentally found that these descriptors perform competitively with respect to state-of-the art descriptors. Further elaboration of the proposed descriptors and additional experimentation is required to better assess their potential.
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
Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: A review. ACM Computing Surveys 43(3), 16:1–16:43 (2011)
Agustí, P., Traver, V.J., Marin-Jimenez, M.J., Pla, F.: Exploring alternative spatial and temporal dense representations for action recognition. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part II. LNCS, vol. 6855, pp. 364–371. Springer, Heidelberg (2011)
Ali, S., Shah, M.: Human action recognition in videos using kinematic features and multiple instance learning. PAMI 32(2), 288–303 (2010)
Bregonzio, M., Xiang, T., Gong, S.: Fusing appearance and distribution information of interest points for action recognition. PR 45(3), 1220–1234 (2012)
Chakraborty, B., Holte, M.B., Moeslund, T.B., González, J.: Selective spatio-temporal interest points. CVIU 116(3), 396–410 (2012)
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)
Al Ghamdi, M., Al Harbi, N., Gotoh, Y.: Spatio-temporal video representation with locality-constrained linear coding. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part III. LNCS, vol. 7585, pp. 101–110. Springer, Heidelberg (2012)
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. PAMI 29(12), 2247–2253 (2007)
Laptev, I.: On space-time interest points. International Journal of Computer Vision 64(2-3), 107–123 (2003)
Li, R., Zickler, T.: Discriminative virtual views for cross-view action recognition. In: CVPR (2012)
Marwan, N., Romano, M.C., Thiel, M., Kurthss, J.: Recurrence plots for the analysis of complex systems. Physics Reports 438(5-6), 237–329 (2007)
Poppe, R.: A survey on vision-based human action recognition. Image Vision Computer 28(6), 976–990 (2010)
Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local SVM approach. In: ICPR, pp. 32–36 (2004)
Jong Seo, H., Milanfar, P.: Action recognition from one example. PAMI 33(5), 867–882 (2011)
Wang, H., Ullah, M.M., Kläser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC (2009)
Wu, X., Jia, Y.: View-invariant action recognition using latent kernelized structural SVM. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 411–424. Springer, Heidelberg (2012)
Zhang, Y., Liu, X., Chang, M.-C., Ge, W., Chen, T.: Spatio-temporal phrases for activity recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 707–721. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Agustí, P., Traver, V.J., Pla, F. (2013). Characterizing Actions with Local Descriptors Based on Kinematics and Flow Recurrences. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-38628-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
eBook Packages: Computer ScienceComputer Science (R0)