Share Email Print

Proceedings Paper

Gaussian mixtures for anomaly detection in crowded scenes
Author(s): Habib Ullah; Lorenza Tenuti; Nicola Conci
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 19 March 2013
PDF: 6 pages
Proc. SPIE 8663, Video Surveillance and Transportation Imaging Applications, 866303 (19 March 2013); doi: 10.1117/12.2003893
Show Author Affiliations
Habib Ullah, Univ. degli Studi di Trento (Italy)
Lorenza Tenuti, Univ. degli Studi di Trento (Italy)
Nicola Conci, Univ. degli Studi di Trento (Italy)

Published in SPIE Proceedings Vol. 8663:
Video Surveillance and Transportation Imaging Applications
Robert Paul Loce; Eli Saber, Editor(s)

© SPIE. Terms of Use
Back to Top