Incremental and adaptive abnormal behaviour detection ?
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.
abnormality detection visual surveillance likelihood ratio test behaviour analysis and recognition incremental learning dynamic scene modelling dynamic bayesian networks