Unusual human activity detection using Markov Logic Networks

In this paper we explore detection of unusual activities using Markov Logic Network (MLN) based approach. Any human activity which is in variance from a defined usual set attracts human attention and is considered unusual. Such activities include anomaly detection in crowds, some repetition or omission of subactivities in a given sequence of activities in Ambient Assisted Living environments or an outlier in case of surveillance. In this paper, we target the unusual activities occurring in workplaces. Typical usual activities considered are: entering a room, walking, sitting down and working. We define activities with unlabeled actions as well as outliers as unusual activities. The outliers include activities with repeated actions and activities with certain actions omitted. We use Markov Logic Network because it allows us to create common sense rules defining the relationship between different actions and activities. We validate our results on a noisy data set.