Gaussian mixtures for anomaly detection in crowded scenes

Habib Ullah; Lorenza Tenuti; Nicola Conci

doi:10.1117/12.2003893

19 March 2013 Gaussian mixtures for anomaly detection in crowded scenes

Habib Ullah, Lorenza Tenuti, Nicola Conci

Proceedings Volume 8663, Video Surveillance and Transportation Imaging Applications; 866303 (2013) https://doi.org/10.1117/12.2003893
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

In this paper, we propose a fast and robust framework for anomaly detection in crowed scenes. In our method, anomaly is adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. For this purpose, we extract motion features by repeatedly initializing a grid of particles over a temporal window. These features are exploited in a real-time anomaly detection system. In order to model the ordinary behavior of the people moving in the crowd, we use the Gaussian mixture model (GMM) technique, which is robust enough to capture the scene dynamics. As opposed to explicitly modeling the values of all the pixels as a mixture of Gaussians, we adopted the GMM to learn the behavior of the motion features extracted from the particles. Based on the persistence and the variance of each Gaussian distribution, we determine which Gaussians can be associated to the normal behavior of the crowd. Particles with motion features that do not fit the distributions representing normal behavior are signaled as anomaly, until there is a Gaussian able to include them with sufficient evidence supporting it. Experiments are extensively conducted on publically available benchmark dataset, and also on a challenging dataset of video sequences we captured. The experimental results revealed that the proposed method performs effectively for anomaly detection.

Citation Download Citation

Habib Ullah, Lorenza Tenuti, and Nicola Conci "Gaussian mixtures for anomaly detection in crowded scenes", Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 866303 (19 March 2013); https://doi.org/10.1117/12.2003893

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