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Anomaly Detection in Crowded Scenes: A Novel Framework Based on Swarm Optimization and Social Force Modeling

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Modeling, Simulation and Visual Analysis of Crowds

Part of the book series: The International Series in Video Computing ((VICO,volume 11))

Abstract

This chapter presents a novel scheme for analyzing the crowd behavior from visual crowded scenes. 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. We then use this proposed particle advection scheme to detect both global and local anomaly events in the crowded scene. 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.

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Acknowledgements

This article summarizes and incorporates two earlier publications concerning global [26] and local [25] anomaly detection in crowded scenarios.

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Authors and Affiliations

  1. Pattern Analysis and Computer Vision (PAVIS), Istituto Italiano di Tecnologia, via Morego 30, 16163, Genova, Italy

    R. Raghavendra, M. Cristani, A. Del Bue, E. Sangineto & V. Murino

Authors
  1. R. Raghavendra
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  2. M. Cristani
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  3. A. Del Bue
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  4. E. Sangineto
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  5. V. Murino
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Corresponding author

Correspondence to V. Murino .

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Editors and Affiliations

  1. Center for Vision Technologies, SRI International, Princeton, New Jersey, USA

    Saad Ali

  2. Department of Computer Science, Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  3. Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA

    Dinesh Manocha

  4. Center for Research in Computer Vision, University of Central Florida, Orlando, Florida, USA

    Mubarak Shah

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Raghavendra, R., Cristani, M., Del Bue, A., Sangineto, E., Murino, V. (2013). 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. The International Series in Video Computing, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8483-7_15

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  • DOI: https://doi.org/10.1007/978-1-4614-8483-7_15

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8482-0

  • Online ISBN: 978-1-4614-8483-7

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