Incremental Visual Behaviour Modelling

We develop a novel visual behaviour modelling approach that performs incremental and adaptive behaviour model learning for online abnormality detection. Three key features make our approach advantageous over previous ones: (1) unsupervised learning, (2) online and incremental model construction, and (3) model adaptation to changes in visual context. In particular, we formulate an incremental EM algorithm with added model adaptation capacity for online behaviour model learning. These features are not only desirable but also necessary for processing large volume of unlabelled surveillance video data with changes of visual context over time. It has been demonstrated by our experiments that our incrementally learned behaviour models are superior to those learned in batch mode in terms of both performance in abnormality detection and computational efficiency.