This paper describes a framework for detecting unusual events in surveillance videos. Most surveillance systems consist of multiple video streams, but traditional event detection systems treat individual video streams independently or combine them in the feature extraction level through geometric reconstruction. Our framework combines multiple video streams in the inference level, with a coupled hidden Markov Model (CHMM). We use two-stage training to bootstrap a set of usual events, and train a CHMM over the set. By thresholding the likelihood of a test segment being generated by the model, we build a unusual event detector.
We evaluate the performance of our detector through qualitative and quantitative experiments on two sets of real world videos.