Unusual event detection using sparse spatio-temporal features and bag of words model

We present a system for unusual event detection in single fixed camera surveillance video. Instead of taking a binary or multi-class supervised learning approach, we take a one-class classification approach assuming that training dataset only contains usual events. The videos are modeled using a bag of words model for documents, where the words are prototypical sparse spatio-temporal feature descriptors extracted along moving objects in the scene of observation. We learn a probabilistic model of the training data as a corpus of documents, which contains a certain probabilistic mixture of latent topics, using Latent Dirichlet Allocation framework. In this framework, topics are further modeled as certain probabilistic mixture of words. Unusual events are video clips that probabilistically deviate more than a threshold from the distribution of the usual events. Our results indicate potential to learn usual events from a few examples, reliable flagging of unusual events, and sufficient speed for practical applications.