Abstract
This paper presents a new solution for path analysis using minimal path techniques with external directional forces. Previously techniques presented in the literature need to store every different path that exists in the scene. This is a problem in terms of memory. They also need the complete route to perform the computation, being unable to be used detecting uncommon events, like accidents, in real time. We introduce a path planning technique that, using only a velocity field, is able to cope with these problems. The technique can be used with no information a priori about the environment, while it is possible to include or even modified it. A case of study based on traffic analysis is presented to show the performance of the methodology. A complex turnaround scene along with highway real data tested our methodology, showing promising results.
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
Bouguet, J.: Pyramidal implementation of the lucas kanade feature tracker. Intel Corporation, Microprocessor Research Labs (2000)
Calderara, S., Cucchiara, R., Prati, A.: Detection of abnormal behaviors using a mixture of von mises distributions. In: IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007, pp. 141–146 (September 2007)
Cancela, B., Ortega, M., Fernández, A., Penedo, M.G.: Path Analysis in Multiple-Target Video Sequences. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part II. LNCS, vol. 6979, pp. 50–59. Springer, Heidelberg (2011)
Cohen, L.D., Kimmel, R.: Global minimum for active contour models: A minimal path approach. International Journal of Computer Vision 24, 57–78 (1997)
Elston, J., Stachura, M., Frew, E., Herzfeld, U.: Toward model free atmospheric sensing by aerial robot networks in strong wind fields. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 369–374 (May 2009)
Johnson, N., Hogg, D.: Learning the distribution of object trajectories for event recognition. Image and Vision Computing 14(8), 609–615 (1996)
Junejo, I., Javed, O., Shah, M.: Multi feature path modeling for video surveillance. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 2, pp. 716–719 (August 2004)
Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proc. 6th Int. Conf. on Knowledge Discovery and Data Mining, pp. 285–289 (2000)
Makris, D., Ellis, T.: Learning semantic scene models from observing activity in visual surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35(3), 397–408 (2005)
Moore, B.E., Ali, S., Mehran, R., Shah, M.: Visual crowd surveillance through a hydrodynamics lens. Commun. ACM 54(12), 64–73 (2011)
Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 312–319 (2009)
Porikli, F., Haga, T.: Event detection by eigenvector decomposition using object and frame features. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2004., p. 114 (June 2004)
Saleemi, I., Shafique, K., Shah, M.: Probabilistic modeling of scene dynamics for applications in visual surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 31(8), 1472–1485 (2009)
Sethian, J.A., Vladimirsky, A.: Ordered Upwind Methods for Static Hamilton–Jacobi Equations: Theory and Algorithms. SIAM Journal on Numerical Analysis 41(1), 325–363 (2003)
Tsitsiklis, J.N.: Efficient algorithms for globally optimal trajectories. IEEE Transactions on Automatic Control 40, 1528–1538 (1995)
Wang, X., Ma, K., Ng, G.-W., Grimson, W.: Trajectory analysis and semantic region modeling using nonparametric hierarchical bayesian models. International Journal of Computer Vision 95, 287–312 (2011)
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
Cancela, B., Ortega, M., Penedo, M.G. (2013). Path Analysis Using Directional Forces. A Practical Case: Traffic Scenes. 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_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-38628-2_43
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)