Detecting abnormal events via hierarchical dirichlet processes

Detecting abnormal event from video sequences is an important problem in computer vision and pattern recognition and a large number of algorithms have been devised to tackle this problem. Previous state-based approaches all suffer from the problem of deciding the appropriate number of states and it is often difficult to do so except using a trial-and-error approach, which may be infeasible in real-world applications. Yet in this paper, we have proposed a more accurate and flexible algorithm for abnormal event detection from video sequences. Our three-phase approach first builds a set of weak classifiers using Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), and then proposes an ensemble learning algorithm to filter out abnormal events. In the final phase, we will derive abnormal activity models from the normal activity model to reduce the FP (False Positive) rate in an unsupervised manner. The main advantage of our algorithm over previous ones is to naturally capture the under ing feature in abnormal event detection via HDP-HMM. Experimental results on a real-world video sequence dataset ave shown the effectiveness of our algorithm. © Springer-Verlag Berlin Heidelberg 2009