Signal, Image and Video Processing

, Volume 9, Issue 7, pp 1669–1677 | Cite as

Moving horizon estimation of pedestrian interactions using multiple velocity fields

  • Ana Portelo
  • Mário A. T. Figueiredo
  • João M. Lemos
  • Jorge S. Marques
Original Paper

Abstract

This paper describes a model, and algorithms based on it, for the analysis of pedestrian interactions in outdoor scenes. Pedestrian activities are described by their trajectories in the scene, and we wish to know if they were independently generated or if they are correlated. Two models are considered: (i) a model based on multiple velocity fields recently proposed in Nascimento et al. (IEEE Trans Image Process 22(5):1712–1725, 2013) and (ii) an interaction model based on the attraction/repulsion between pairs of pedestrians. Several combinations of these models are studied and evaluated. An estimation method based on a moving horizon optimization of a quadratic cost functional is proposed. Experimental results with synthetic data and real video data are presented to assess the performance of the algorithms.

Keywords

Human activity recognition  Trajectory analysis Probabilistic models 

References

  1. 1.
    Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. (CSUR) Surv. Homepage Arch. 43(3) (2011)Google Scholar
  2. 2.
    Turaga, P., Subrahmanian, R., Subrahmanian, V., Udrea, O.: Machine recognition of human activities: a survey. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1473–1488 (2008)CrossRefGoogle Scholar
  3. 3.
    Oliver, N., Rosario, B., Pentland, A.: A bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)CrossRefGoogle Scholar
  4. 4.
    Suk, H.-I., Jain, A., Lee, S.-W.: A network of dynamic probabilistic models for human interaction analysis. IEEE Trans. Circuits Syst. Video Technol. 21(7), 932–945 (2011)CrossRefGoogle Scholar
  5. 5.
    Ntalampiras, S., Arsic, D., Hofmann, M., Andersson, M., Ganchev, T.: PROMETHEUS: Heterogeneous Sensor Database in Support of Human Behavioral Patterns in Unrestricted Environments. Signal, Image and Video Processing. Springer (2012) Google Scholar
  6. 6.
    Mahbub, U., Imtiaz, H., Ahad, A.R.: Action recognition based on statistical analysis from clustered flow vectors. Signal, Image and Video Processing. Springer (2013)Google Scholar
  7. 7.
    Nascimento, J., Figueiredo, M.A.T., Marques, J.: Activity recognition using mixture of vector fields. IEEE Trans. Image Process. 22(5), 1712–1725 (2013)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Nascimento, J., Marques, J., Figueiredo, M.A.T.: Classification of complex pedestrian activities from trajectories. IEEE International Conference Image Processing, pp. 3481–3484 (2010)Google Scholar
  9. 9.
    Helbing, D.: A mathematical model for the behavior of pedestrians. Behav. Sci. 36, 298–310 (1991)CrossRefGoogle Scholar
  10. 10.
    Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51, 4282–4286 (1995)CrossRefGoogle Scholar
  11. 11.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE International Conf. on Computer Vision and Pattern Recognition, pp. 935–942 (2009)Google Scholar
  12. 12.
    Zhang, Y., Qin, L., Yao, H., Huang, Q.: Abnormal crowd behavior detection based on social attribute-aware force model. In: IEEE Internatinal Conference Image Processing, pp. 2689–2692 (2012)Google Scholar
  13. 13.
    Henderson, L.F.: On the fluid mechanic of human crowd motions. Transp. Res. 8, 509–515 (1974)CrossRefGoogle Scholar
  14. 14.
    Okazaki, S.: A study of pedestrian movement in architectural space, part 1: pedestrian movement by the application on of magnetic models. Trans. A.I.J 283, 111–119 (1979)Google Scholar
  15. 15.
    Luber, M., Stork, J., Tipaldi, G., Arras, K.O.: People tracking with human motion predictions from social forces. In: IEEE Conference on ICRA, pp. 464–469 (2010)Google Scholar
  16. 16.
    Liu, X., Chua, C.: Multi-agent activity recognition using observation decomposed hidden markov models. Image Vis. Comput. 24, 166–175 (2006)Google Scholar
  17. 17.
    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)CrossRefMathSciNetMATHGoogle Scholar
  18. 18.
    Veenman, C.J., Reinders, M.J.T., Backer, E.: Resolving motion correspondence for densely moving points. IEEE Trans. Pattern Anal. Mach. Intell. 23, 54–72 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Ana Portelo
    • 1
  • Mário A. T. Figueiredo
    • 2
  • João M. Lemos
    • 1
  • Jorge S. Marques
    • 3
  1. 1.INESC-IDInstituto Superior TecnicoLisbonPortugal
  2. 2.Instituto de TelecomunicaçõesInstituto Superior TecnicoLisbonPortugal
  3. 3.Institute for Systems and RoboticsInstituto Superior TecnicoLisbonPortugal

Personalised recommendations