Identifying surprising events in video using bayesian topic models

In this paper we focus on the problem of identifying interesting parts of the video. To this end we employ the notion of Bayesian surprise, as defined in [9, 10], in which an event is considered surprising if its occurrence leads to a large change in the probability of the world model. We propose to compute this abstract measure of surprise by first modeling a corpus of video events using the Latent Dirichlet Allocation model. Subsequently, we measure the change in the Dirichlet prior of the LDA model as a result of each video event's occurrence. This leads to a closed form expression for an event's level of surprise. We tested our algorithm on a real world video data, taken by a camera observing an urban street intersection. The results demonstrate our ability to detect atypical events, such as a car making a U-turn or a person crossing an intersection diagonally.