Abstract

We address the problem of visual event recognition in surveil- lance where noise and missing observations are serious problems. Common sense domain knowledge is exploited to overcome them. The knowledge is represented as first-order logic production rules with associated weights to indicate their confidence. These rules are used in combination with a re- laxed deduction algorithm to construct a network of grounded atoms, the Markov Logic Network. The network is used to perform probabilistic infer- ence for input queries about events of interest. The system’s performance is demonstrated on a number of videos from a parking lot domain that con- tains complex interactions of people and vehicles.