Abstract
A human behavior recognition method with an application to political speech videos is presented. We focus on modeling the behavior of a subject with a conditional random field (CRF). The unary terms of the CRF employ spatiotemporal features (i.e., HOG3D, STIP and LBP). The pairwise terms are based on kinematic features such as the velocity and the acceleration of the subject. As an exact solution to the maximization of the posterior probability of the labels is generally intractable, loopy belief propagation was employed as an approximate inference method. To evaluate the performance of the model, we also introduce a novel behavior dataset, which includes low resolution video sequences depicting different people speaking in the Greek parliament. The subjects of the Parliament dataset are labeled as friendly, aggressive or neutral depending on the intensity of their political speech. The discrimination between friendly and aggressive labels is not straightforward in political speeches as the subjects perform similar movements in both cases. Experimental results show that the model can reach high accuracy in this relatively difficult 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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Candamo, J., Shreve, M., Goldgof, D.B., Sapper, D.B., Kasturi, R.: Understanding transit scenes: A survey on human behavior-recognition algorithms. IEEE Transactions on Intelligent Transportation Sysstems 11(1), 206–224 (2010)
Domke, J.: Learning graphical model parameters with approximate marginal inference. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(10), 2454–2467 (2013)
Fathi, A., Hodgins, J.K., Rehg, J.M.: Social interactions: A first-person perspective. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, USA, pp. 1226–1233 (2012)
Kläser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: Proc. British Machine Vision Conference, University of Leeds, Leeds, UK, pp. 995–1004 (September 2008)
Lafferty, J.D., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conference on Machine Learning, Williams College, Williamstown, MA, USA, pp. 282–289 (2001)
Lan, T., Sigal, L., Mori, G.: Social roles in hierarchical models for human activity recognition. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, USA, pp. 1354–1361 (2012)
Laptev, I.: On space-time interest points. International Journal of Computer Vision 64(2-3), 107–123 (2005)
Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: An empirical study. In: Proc. Uncertainty in Artificial Intelligence, Stockholm, Sweden, pp. 467–475 (1999)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28(6), 976–990 (2010)
Prince, S.J.D.: Computer Vision: Models Learning and Inference. Cambridge University Press (2012)
Ramanathan, V., Yao, B., Fei-Fei, L.: Social role discovery in human events. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Portland, OR, USA (June 2013)
Smeulders, A.W.M., Chu, D.M., Cucchiara, R., Calderara, S., Dehghan, A., Shah, M.: Visual tracking: An experimental survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(PrePrints), 1 (2013)
Tran, K.N., Bedagkar-Gala, A., Kakadiaris, I.A., Shah, S.K.: Social cues in group formation and local interactions for collective activity analysis. In: Proc. 8th International Conference on Computer Vision Theory and Applications, Barcelona, Spain, pp. 539–548 (February 2013)
Vrigkas, M., Karavasilis, V., Nikou, C., Kakadiaris, I.A.: Action recognition by matching clustered trajectories of motion vectors. In: Proc. 8th International Conference on Computer Vision Theory and Applications, Barcelona, Spain, pp. 112–117 (February 2013)
Vrigkas, M., Nikou, C., Kakadiaris, I.A.: The Parliament database (2014), http://www.cs.uoi.gr/~mvrigkas/Parliament.html
Wang, H., Kläser, A., Schmid, C., Cheng-Lin, L.: Action recognition by dense trajectories. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, United States, pp. 3169–3176 (2011)
Yan, X., Luo, Y.: Recognizing human actions using a new descriptor based on spatial-temporal interest points and weighted-output classifier. Neurocomputing 87, 51–61 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Vrigkas, M., Nikou, C., Kakadiadis, I.A. (2014). Classifying Behavioral Attributes Using Conditional Random Fields. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-07064-3_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07063-6
Online ISBN: 978-3-319-07064-3
eBook Packages: Computer ScienceComputer Science (R0)