Unsupervised Learning of Event Classes from Video

We present a method for unsupervised learning of event classes from videos in which multiple actions might occur simultaneously. It is assumed that all such activities are produced from an underlying set of event class generators. The learning task is then to recover this generative process from visual data. A set of event classes is derived from the most likely decomposition of the tracks into a set of labelled events involving subsets of interacting tracks. Interactions between subsets of tracks are modelled as a relational graph structure that captures qualitative spatio-temporal relationships between these tracks. The posterior probability of candidate solutions favours decompositions in which events of the same class have a similar relational structure, together with other measures of well-formedness. A Markov Chain Monte Carlo (MCMC) procedure is used to efficiently search for the MAP solution. This search moves between possible decompositions of the tracks into sets of unlabell...

• Type: Conference
• Year: 2010
• Where: AAAI
• Authors: Muralikrishna Sridhar, Anthony G. Cohn, David C. Hogg
• Keywords: AAAI 2010, Event Class Generators, Event Classes, Events Involving Subsets, Intelligent Agents