Advertisement

Real-Time Detection and Simulation of Abnormal Crowd Behavior

  • Wilbert G. AguilarEmail author
  • Marco A. Luna
  • Julio F. Moya
  • Marco P. Luna
  • Vanessa Abad
  • Hugo Ruiz
  • Humberto Parra
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10325)

Abstract

In this paper, we propose an algorithm for abnormal crowd behavior detection and simulation for real time surveillance applications. Our method is a low computational cost approach based on moved pixel density modelling. Using statistical methods, we obtain the model of pixel densities in normal behaviors based on datasets available in the literature. During abnormal anomalous event detection we run a simulation of people motion and save the data for future analysis. We test the execution time of our algorithm for motion detection to validate its usage in fast applications. Finally we validate our method comparing it with other approaches in the literature in two datasets.

Keywords

HAAR HOG LBP Saliency maps People detection Cascade classifiers UAVs 

Notes

Acknowledgement

This work is part of the projects VisualNavDrone 2016-PIC-024 and MultiNavCar 2016-PIC-025, from the Universidad de las Fuerzas Armadas ESPE, directed by Dr. Wilbert G. Aguilar.

References

  1. 1.
    Popoola, O.P., Wang, K.: Video-based abnormal human behavior recognition—a review. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 865–878 (2012)CrossRefGoogle Scholar
  2. 2.
    Lu, C., Shi, J., Jia, J.: Abnormal event detection at 150 fps in matlab. In: Proceeding IEEE International Conference on Computer Vision, pp. 2720–2727 (2013)Google Scholar
  3. 3.
    Aguilar, W.G., Luna, M.A., Moya, J.F., Abad, V., Parra, H., Ruiz, H.: Pedestrian detection for UAVs using cascade classifiers with meanshift. In: IEEE 11th International Conference on Semantic Computing (ICSC), pp. 509–514 (2017)Google Scholar
  4. 4.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. Comput. Vis. Pattern (2009)Google Scholar
  5. 5.
    Aguilar, W.G., Alulema, D., Limaico, A., Sandoval, D.: Development and verification of a verbal corpus based on natural language for Ecuadorian Dialect. In: IEEE 11th International Conference on Semantic Computing (ICSC), pp. 515–519 (2017)Google Scholar
  6. 6.
    Aguilar, W.G., Angulo, C.: Real-time model-based video stabilization for microaerial vehicles. Neural Process. Lett. 43(2), 459–477 (2016)CrossRefGoogle Scholar
  7. 7.
    Aguilar, W.G., Angulo, C.: Real-time video stabilization without phantom movements for micro aerial vehicles. EURASIP J. Image Video Process. 2014(1), 46 (2014)CrossRefGoogle Scholar
  8. 8.
    Jacques Jr., J.S., Musse, S., Jung, C.: Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 27(5), 66–77 (2010)Google Scholar
  9. 9.
    Cabras, P., Rosell, J., Pérez, A., Aguilar, W.G., Rosell, A.: Haptic-based navigation for the virtual bronchoscopy. IFAC Proc. 18(1), 9638–9643 (2011)CrossRefGoogle Scholar
  10. 10.
    Aguilar, W., Morales, S.: 3D environment mapping using the kinect V2 and path planning based on RRT algorithms. Electronics 5(4), 70 (2016)CrossRefGoogle Scholar
  11. 11.
    Lemercier, S., Jelic, A., Kulpa, R., Hua, J., Fehrenbach, J., Degond, P., Appert-Rolland, C., Donikian, S., Pettré, J.: Realistic following behaviors for crowd simulation. EUROGRAPHICS 31(2) (2012)Google Scholar
  12. 12.
    Raghavendra, R., Cristani, M., Bue, A., Sangineto, E., Murino, V.: Anomaly detection in crowded scenes: a novel framework based on swarm optimization and social force modeling. In: Ali, S., Nishino, K., Manocha, D., Shah, M. (eds.) Modeling, Simulation and Visual Analysis of Crowds. TISVC, vol. 11, pp. 383–411. Springer, New York (2013). doi: 10.1007/978-1-4614-8483-7_15 CrossRefGoogle Scholar
  13. 13.
    Basharat, A., Gritai, A., Shah, M.: Learning object motion patterns for anomaly detection and improved object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  14. 14.
    Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)CrossRefGoogle Scholar
  15. 15.
    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1975–1981 (2010)Google Scholar
  16. 16.
    Wu, S., Moore, B.E., Shah, M.: Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: Computer Vision and Pattern Recognition, pp. 2054–2060 (2010)Google Scholar
  17. 17.
    Mehran, R., Moore, Brian E., Shah, M.: A streakline representation of flow in crowded scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 439–452. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15558-1_32 CrossRefGoogle Scholar
  18. 18.
    Ali, S., Shah, M.: A lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: Computer Vision and Pattern Recognition 2007 (2007)Google Scholar
  19. 19.
    Andrade, E.L., Blunsden, S., Fisher, R.B.: Modelling crowd scenes for event detection. In: 18th International Conference Pattern Recognition, ICPR 2006, vol. 1, pp. 175–178 (2006)Google Scholar
  20. 20.
    Ke, Y., Sukthankar, R., Hebert, M.: Event detection in crowded videos. In: IEEE 11th International Conference Computer Vision, ICCV 2007, pp. 1–8 (2007)Google Scholar
  21. 21.
    Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: IEEE Conference Computer Vision Pattern Recognition, pp. 1446–1453 (2009)Google Scholar
  22. 22.
    Zivkovic, Z., Van Der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)CrossRefGoogle Scholar
  23. 23.
    Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 555–560 (2008)CrossRefGoogle Scholar
  24. 24.
    Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790–2797 (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wilbert G. Aguilar
    • 1
    • 4
    • 5
    Email author
  • Marco A. Luna
    • 2
  • Julio F. Moya
    • 2
  • Marco P. Luna
    • 3
    • 7
  • Vanessa Abad
    • 6
  • Hugo Ruiz
    • 1
    • 8
  • Humberto Parra
    • 1
    • 7
  1. 1.Dep. Seguridad y DefensaUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  2. 2.Dep. Eléctrica y ElectrónicaUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  3. 3.Dep. Tierra y ConstrucciónUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  4. 4.CICTE Research CenterUniversidad de las Fuerzas Armadas ESPESangolquíEcuador
  5. 5.GREC Research GroupUniversitat Politècnica de CatalunyaBarcelonaSpain
  6. 6.Universitat de BarcelonaBarcelonaSpain
  7. 7.PLM Research CenterPurdue UniversityWest LafayetteUSA
  8. 8.Universidad Politécnica de MadridMadridSpain

Personalised recommendations