In recent years, there has been an increasing focus on detecting anomalous events in surveillance applications. In this paper, we present an unsupervised feature-based abnormality detection algorithm suited for online video surveillance applications. The features used in our method include trajectories, object sizes, and velocities. Unlike the traditional trajectory-based abnormality detection, we consider both the trajectory-based information and region-based information. In our algorithm, the trajectories are clustered using Principal Component Analysis (PCA), providing the ability to choose the optimal number of clusters. Different trajectory clusters are modelled as a chain of Gaussians and new tracks are matched with the cluster models to detect abnormalities. In addition, a novel region-based method is proposed and can be combined with trajectory-based detection. The proposed method has the advantage of detecting abnormal events that cannot be detected by trajectory-based algorithms alone. The results show improved detection compared with traditional trajectory-based methods. (5 pages)