Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Augmented Reality to Enhance the Clinician’s Observation During Assessment of Daily Living Activities Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Real-Time Detection and Simulation of Abnormal Crowd Behavior

verfasst von : Wilbert G. Aguilar, Marco A. Luna, Julio F. Moya, Marco P. Luna, Vanessa Abad, Hugo Ruiz, Humberto Parra

Erschienen in: Augmented Reality, Virtual Reality, and Computer Graphics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Real-Time 3D Modeling with a RGB-D Camera and On-Board Processing
Nächstes Kapitel Steering Versus Teleport Locomotion for Head Mounted Displays
Literatur
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)CrossRef
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)
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)
4.
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. Comput. Vis. Pattern (2009)
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)
6.
Aguilar, W.G., Angulo, C.: Real-time model-based video stabilization for microaerial vehicles. Neural Process. Lett. 43(2), 459–477 (2016)CrossRef
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)CrossRef
8.
Jacques Jr., J.S., Musse, S., Jung, C.: Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 27(5), 66–77 (2010)
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)CrossRef
10.
Aguilar, W., Morales, S.: 3D environment mapping using the kinect V2 and path planning based on RRT algorithms. Electronics 5(4), 70 (2016)CrossRef
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)
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 CrossRef
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)
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)CrossRef
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)
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)
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 CrossRef
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)
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)
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)
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)
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)CrossRef
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)CrossRef
24.
Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790–2797 (2009)
Metadaten
Titel
Real-Time Detection and Simulation of Abnormal Crowd Behavior
verfasst von
Wilbert G. Aguilar
Marco A. Luna
Julio F. Moya
Marco P. Luna
Vanessa Abad
Hugo Ruiz
Humberto Parra
Copyright-Jahr
2017
Buch
Augmented Reality, Virtual Reality, and Computer Graphics

Print ISBN: 978-3-319-60927-0

Electronic ISBN: 978-3-319-60928-7

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-60928-7_36

Premium Partner

    Bildnachweise