Data fusion approach to obstacle detection and identification

Multisensor data fusion is applied to the problem of detecting and identifying obstacles in a static (or slowly-changing) known scene. Automatic detection of unexpected objects is of crucial importance in reducing the need for personnel in surveillance stations: possible applications to the area of rail transportation systems are currently being explored, and results for a level crossing monitoring situation are presented. This paper defines a framework that allows the exploitation of multiple sensors or multiple operation modes of a single sensor: as an example, it describes a way of merging the data coming from two channels (the RG bands of a color video camera), each providing two intensity images (the actual scene and the "normal" background). Moreover, the system can profit by the introduction of additional sensors, like a Laser Range Finder to aid in locating obstacles in 3D space. The proposed system architecture is based on a blackboard organization for both inference and control: particular care has been exercised in optimizing the data flow through system modules by means of a heterarchical control structure. Object-oriented programming is extensively used to isolate the system's basic units in order to allow a future parallel implementation. © 1989 SPIE.