Applying Bayes Markov chains for the detection of ATM related scenarios

Video surveillance systems have been introduced in various fields of our daily life to enhance security and protect individuals and sensitive infrastructure. Up to now it has been usually utilized as a forensic tool for after the fact investigations and are commonly monitored by human operators. In order to assist these and to be able to react in time, a fully automated system is desired. In this work we will present a multi camera surveillance system, which is required to resolve heavy occlusions, to detect robberies at ATM machines. The resulting trajectories will be analyzed for so called Low Level Activities (LLA), such as walking, running and stationarity, applying simple but robust approaches. The results of the LLA analysis will subsequently be fed into a Bayesian Network, that is used as a stochastic model to model so called High Level Activities (HLA). Introducing state transitions between HLAs will allow a temporal modeling of a complex scene. This can be represented by a Markovian process.