Anomaly detection in crowded scenes

A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: (1) joint modeling of appearance and dynamics of the scene, and the abilities to dete...

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Vydané v:2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition s. 1975 - 1981
Hlavní autori: Mahadevan, Vijay, Weixin Li, Bhalodia, Viral, Vasconcelos, Nuno
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2010
Predmet:
Cameras
Computer vision
Hidden Markov models
Histograms
Image motion analysis
Layout
Linear discriminant analysis
Monitoring
Surveillance
Video sequences
ISBN:1424469848, 9781424469840
ISSN:1063-6919, 1063-6919
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Shrnutí:A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: (1) joint modeling of appearance and dynamics of the scene, and the abilities to detect (2) temporal, and (3) spatial abnormalities. The model for normal crowd behavior is based on mixtures of dynamic textures and outliers under this model are labeled as anomalies. Temporal anomalies are equated to events of low-probability, while spatial anomalies are handled using discriminant saliency. An experimental evaluation is conducted with a new dataset of crowded scenes, composed of 100 video sequences and five well defined abnormality categories. The proposed representation is shown to outperform various state of the art anomaly detection techniques.
ISBN:1424469848
9781424469840
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2010.5539872