Article

Abnormal crowd behavior detection by social force optimization

Authors:

R. Raghavendra,

Alessio Del Bue,

Marco Cristani,

Vittorio MurinoAuthors Info & Claims

HBU'11: Proceedings of the Second international conference on Human Behavior Unterstanding

Pages 134 - 145

https://doi.org/10.1007/978-3-642-25446-8_15

Published: 16 November 2011 Publication History

Abstract

We propose a new scheme for detecting and localizing the abnormal crowd behavior in video sequences. The proposed method starts from the assumption that the interaction force, as estimated by the Social Force Model (SFM), is a significant feature to analyze crowd behavior. We step forward this hypothesis by optimizing this force using Particle Swarm Optimization (PSO) to perform the advection of a particle population spread randomly over the image frames. The population of particles is drifted towards the areas of the main image motion, driven by the PSO fitness function aimed at minimizing the interaction force, so as to model the most diffused, normal, behavior of the crowd. In this way, anomalies can be detected by checking if some particles (forces) do not fit the estimated distribution, and this is done by a RANSAC-like method followed by a segmentation algorithm to finely localize the abnormal areas. A large set of experiments are carried out on public available datasets, and results show the consistent higher performances of the proposed method as compared to other state-of-the-art algorithms, proving the goodness of the proposed approach.

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Cited By

Nguyen TLy NThang HHu ZBui MSikdar BIDE IBinh HEngchuan WSang DOanh N(2017)Abnormal Activity Detection based on Dense Spatial-Temporal Features and Improved One-Class LearningProceedings of the 8th International Symposium on Information and Communication Technology10.1145/3155133.3155147(370-377)Online publication date: 7-Dec-2017
https://dl.acm.org/doi/10.1145/3155133.3155147
Liu PTao YZhao WTang X(2017)Abnormal crowd motion detection using double sparse representationNeurocomputing10.1016/j.neucom.2016.09.138269:C(3-12)Online publication date: 20-Dec-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.138
Zitouni MBhaskar HDias JAl-Mualla M(2016)Advances and trends in visual crowd analysisNeurocomputing10.1016/j.neucom.2015.12.070186:C(139-159)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.12.070
Show More Cited By

Abnormal crowd behavior detection by social force optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Computer graphics
    1. Animation

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Information & Contributors

Information

Published In

cover image Guide Proceedings

HBU'11: Proceedings of the Second international conference on Human Behavior Unterstanding

November 2011

158 pages

ISBN:9783642254451

Editors:
Albert Ali Salah
Department of Computer Engineering, Bogaziçi University, Bebek, Istanbul, Turkey
,
Bruno Lepri
Department of Computer Engineering, FBK - Fondazione Bruno Kessler, Via Sommarive 18, Trento, Istanbul, Italy

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 16 November 2011

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Cited By

Nguyen TLy NThang HHu ZBui MSikdar BIDE IBinh HEngchuan WSang DOanh N(2017)Abnormal Activity Detection based on Dense Spatial-Temporal Features and Improved One-Class LearningProceedings of the 8th International Symposium on Information and Communication Technology10.1145/3155133.3155147(370-377)Online publication date: 7-Dec-2017
https://dl.acm.org/doi/10.1145/3155133.3155147
Liu PTao YZhao WTang X(2017)Abnormal crowd motion detection using double sparse representationNeurocomputing10.1016/j.neucom.2016.09.138269:C(3-12)Online publication date: 20-Dec-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.09.138
Zitouni MBhaskar HDias JAl-Mualla M(2016)Advances and trends in visual crowd analysisNeurocomputing10.1016/j.neucom.2015.12.070186:C(139-159)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.12.070
Wang JXu Z(2016)Spatio-temporal texture modelling for real-time crowd anomaly detectionComputer Vision and Image Understanding10.1016/j.cviu.2015.08.010144:C(177-187)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.cviu.2015.08.010
Bandini SGorrini AVizzari G(2014)Towards an integrated approach to crowd analysis and crowd synthesisPattern Recognition Letters10.1016/j.patrec.2013.10.00344:C(16-29)Online publication date: 15-Jul-2014
https://dl.acm.org/doi/10.1016/j.patrec.2013.10.003
Cristani MRaghavendra RDel Bue AMurino V(2013)Human behavior analysis in video surveillanceNeurocomputing10.1016/j.neucom.2011.12.038100(86-97)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1016/j.neucom.2011.12.038
Conigliaro DSetti FBassetti CFerrario RCristani M(2013)ATTENTOProceedings of 4th International Workshop on Human Behavior Understanding - Volume 821210.1007/978-3-319-02714-2_9(102-111)Online publication date: 22-Oct-2013
https://dl.acm.org/doi/10.1007/978-3-319-02714-2_9

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