Visual surveillance in a dynamic and uncertain world
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A suspicious behaviour detection using a context space model for smart surveillance systems
Computer Vision and Image Understanding
Incremental behavior modeling and suspicious activity detection
Pattern Recognition
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Online detection of abnormal events in video streams
Journal of Electrical and Computer Engineering
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We develop a novel visual behaviour modelling approach that performs incremental and adaptive model learning for online abnormality detection in a visual surveillance scene. The approach has the following key features that make it advantageous over previous ones: (1) Fully unsupervised learning: both feature extraction for behaviour pattern representation and model construction are carried out without the laborious and unreliable process of data labelling. (2) Robust abnormality detection: using Likelihood Ratio Test (LRT) for abnormality detection, the proposed approach is robust to noise in behaviour representation. (3) Online and incremental model construction: after being initialised using a small bootstrapping dataset, our behaviour model is learned incrementally whenever a new behaviour pattern is captured. This makes our approach computationally efficient and suitable for real-time applications. (4) Model adaptation to reflect changes in visual context. Online model structure adaptation is performed to accommodate changes in the definition of normality/abnormality caused by visual context changes. This caters for the need to reclassify what may initially be considered as being abnormal to be normal over time, and vice versa. These features are not only desirable but also necessary for processing large volume of unlabelled surveillance video data with visual context changing over time. The effectiveness and robustness of our approach are demonstrated through experiments using noisy datasets collected from a real world surveillance scene. The experimental results show that our incremental and adaptive behaviour modelling approach is superior to a conventional batch-mode one in terms of both performance on abnormality detection and computational efficiency.