DocumentCode :
3333909
Title :
Online Dominant and Anomalous Behavior Detection in Videos
Author :
Roshtkhari, Mehrsan Javan ; Levine, Martin D.
Author_Institution :
Center for Intell. Machines, McGill Univ., Montreal, QC, Canada
fYear :
2013
fDate :
23-28 June 2013
Firstpage :
2611
Lastpage :
2618
Abstract :
We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
Keywords :
behavioural sciences computing; computational complexity; graph theory; learning (artificial intelligence); object detection; spatiotemporal phenomena; video surveillance; anomalous behavior detection; behavior modeling; contextual graph; hierarchical codebook model; human activity; online dominant behavior detection; scene background; simultaneous online learning; space complexity; spatial event; spatiotemporal contextual information decomposition; spatiotemporal video volume; temporal event; time complexity; video parsing; video surveillance; Change detection algorithms; Clustering algorithms; Context; Probabilistic logic; Three-dimensional displays; Vectors; Videos; Anomaly detection; Bag of video words; Behavior learning; Contextual information; Hierarchical scene modeling; Spatio-Temporal compositions; Surveillance; Video parsing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on
Conference_Location :
Portland, OR
ISSN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2013.337
Filename :
6619181
Link To Document :
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