Detecting abnormal human behaviour using multiple cameras

In this paper a bottom-up approach for human behaviour understanding is presented, using a multi-camera system. The proposed methodology, given a training set of normal data only, classifies behaviour as normal or abnormal, using two different criteria of human behaviour abnormality (short-term behaviour and trajectory of a person). Within this system an one-class support vector machine decides short-term behaviour abnormality, while we propose a methodology that lets a continuous Hidden Markov Model function as an one-class classifier for trajectories. Furthermore, an approximation algorithm, referring to the Forward Backward procedure of the continuous Hidden Markov Model, is proposed to overcome numerical stability problems in the calculation of probability of emission for very long observations. It is also shown that multiple cameras through homography estimation provide more precise position of the person, leading to more robust system performance. Experiments in an indoor environment without uniform background demonstrate the good performance of the system.