In this paper, we propose a framework for unusual event detection. Our approach is motivated by the observation that, while it is unrealistic to obtain a large training data set for unusual events, it is conversely possible to do so for usual events, allowing the creation of a well-estimated model of usual events. In order to overcome the scarcity of training material for unusual events, we propose the use of Bayesian adaptation techniques [14], which adapt a usual event model to produce a number of unusual event models in an unsupervised manner. The proposed framework can thus be considered as a semi-supervised learning technique. In our framework, a new unusual event.
