Abstract
This paper describes a model for the interaction between pedestrians in a scene. We consider a recently proposed model for isolated pedestrians in the scene and extend it by adding an interaction term that accounts for attractive/repulsive behaviors among pedestrians. The proposed model combines multiple velocity fields that represent typical motion regimes in the scene and a time-varying interaction term. The estimation of the active velocity field and interaction parameters is achieved by assuming that they remain constant within every instance of an analysis window that slides in time. This strategy is known as the Moving Horizon Estimation (MHE) method. The proposed algorithm is assessed both by using synthetic data and pedestrian trajectories extracted from video streams.
This work was supported by FCT in the framework of contract PTDC/EEA-CRO/098550/2008, PEst-OE/EEI/LA0009/2011 and PEst-OE/EEI/LA0021/2011.
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
Turaga, P., Chellappa, R., Subrahmanian, V., Udrea, O.: Machine recognition of human activities: A survey. IEEE Trans. on Circuits and Systems for Video Technology 18(11), 1473–1488 (2008)
Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(8), 831–843 (2000)
Suk, H.-I., Jain, A., Lee, S.-W.: A network of dynamic probabilistic models for human interaction analysis. IEEE Transactions on Circuits and Systems for Video Technology 21(7), 932–945 (2011)
Nascimento, J., Figueiredo, M.A.T., Marques, J.: Activity recognition using mixture of vector fields. IEEE Trans. on Image Processing 22(5), 1712–1725 (2013)
Nascimento, J., Marques, J., Figueiredo, M.A.T.: Classification of complex pedestrian activities from trajectories. In: IEEE Int. Conf. Image Processing, pp. 3481–3484 (September 2010)
Helbing, D.: A mathematical model for the behavior of pedestrians. Behavioral Science (36), 298–310 (1991)
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Physical Review (E51), 4282–4286 (1995)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Int. Conf. on Computer Vision and Pattern Recognition, pp. 935–942 (June 2009)
Zhang, Y., Qin, L., Yao, H., Huang, Q.: Abnormal crowd behavior detection based on social attribute-aware force model. In: IEEE Int. Conf. Image Processing, pp. 2689–2692 (September 2012)
Alessandri, A., Baglietto, M., Battistelli, G.: Moving-horizon state estimation for non-linear discrete-time systems: New stability results and approximation schemes. Automatica 44, 1753–1765 (2008)
Veenman, C.J., Reinders, M.J.T., Backer, E.: Resolving motion correspondence for densely moving points. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 54–72 (2001)
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
Portelo, A., Pacheco, S., Figueiredo, M.A.T., Lemos, J.M., Marques, J.S. (2013). Moving Horizon Estimation of Pedestrian Interactions Based on Multiple Velocity Fields. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8034. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41939-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-41939-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41938-6
Online ISBN: 978-3-642-41939-3
eBook Packages: Computer ScienceComputer Science (R0)