@article{ref1,
title="Anomaly detection and localization in crowded scenes",
journal="IEEE transactions on pattern analysis and machine intelligence",
year="2013",
author="Li, Weixin and Mahadevan, Vijay and Vasconcelos, Nuno",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1)~a center-surround discriminant saliency detector that produces spatial saliency scores, and 2)~a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multi-scale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A dataset of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other datasets show that the latter achieves state-of-the-art anomaly detection results.  D3DC9DEA-82C1-48D5-9350-F5D11C82260B<p /> <p>Language: en</p>",
language="en",
issn="0162-8828",
doi="",
url="http://dx.doi.org/"
}