Unsupervised Behaviour Profiling

Abstract

Given a large quantity of unprocessed videos of object activities, the goal of automatic behaviour profiling is to learn a model that is capable of detecting unseen abnormal behaviour patterns whilst recognising novel instances of expected normal behaviour patterns. In this context, an anomaly is defined as an atypical behaviour pattern that is not represented by sufficient examples in previous observations. Behaviour profiling is by unsupervised learning and anomaly detection is treated as a binary classification problem. One of the main challenges for a binary classification model is to differentiate a true anomaly from outliers that give false positives. In this chapter, we consider a clustering model that discovers the intrinsic grouping of behaviour patterns. The method does not require manual data labelling for either feature extraction or discovery of grouping. This is crucial because manual labelling of behaviour patterns is often impractical given the vast amount of video data, and is subject to inconsistency and error prone. The method performs incremental learning to cope with changes of behavioural context. It also detects anomalies on-line so that (a decision on whether a behaviour pattern is normal is made as soon as sufficient visual evidence is collected without the completion of the observed pattern.