Event Modelling and Reasoning with Uncertain Information for Distributed Sensor Networks

CCTV and sensor based surveillance systems are part of our daily lives now in this modern society due to the advances in telecommunications technology and the demand for better security. The analysis of sensor data produces semantic rich events describing activities and behaviours of objects being monitored. Three issues usually are associated with events descriptions. First, data could be collected from multiple sources (e.g., sensors, CCTVs, speedometers, etc). Second, descriptions about these data can be poor, inaccurate or uncertain when they are gathered from unreliable sensors or generated by analysis nonperfect algorithms. Third, in such systems, there is a need to incorporate domain specific knowledge, e.g., criminal statistics about certain areas or patterns, when making inferences. However, in the literature, these three phenomena are seldom considered in CCTV-based event composition models. To overcome these weaknesses, in this paper, we propose a general event modelling and reasoning model which can represent and reason with events from multiple sources including domain knowledge, integrating the Dempster-Shafer theory for dealing with uncertainty and incompleteness. We introduce a notion called event cluster to represent uncertain and incomplete events induced from an observation. Event clusters are then used in the merging and inference process. Furthermore, we provide a method to calculate the mass values of events which use evidential mapping techniques.