Run length encoded Dynamic Bayesian Networks for probabilistic interaction modeling

Human behavior analysis for Cognitive Surveillance Systems (CSS) share mainly the concept that it can be time to extend functionalities beyond simple video analytics. In most recent systems addressed by research, automatic support to human decisions based on object detection, tracking and situation assessment tools is integrated as a part of a complete cognitive artificial process. In such cases a CSS needs to represent complex situations that describe alternative possible real time interactions between the dynamic observed situation and operators´ actions. To obtain such knowledge, particular types of Event based Dynamic Bayesian Networks E-DBNs are here proposed. In this paper it is shown how, by means of Run Length Encoding (RLE) of off line acquired information, the cognitive system is able to represent and anticipate possible operators´ actions within the CSS. Results are shown by considering a crowd monitoring application in a critical infrastructure. A system is presented where a CSS embedding in a structured way RLE E-DBN knowledge can interact with an active visual simulator of crowd situations. Outputs from such a simulator can be easily compared with video signals coming from real cameras and processed by typical Bayesian tracking methods.