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Proceedings Paper

Data Fusion Approach To Obstacle Detection And Identification
Author(s): Giulio Capocaccia; Alberto Damasio; Carlo S. Regazzoni; Gianni Vernazza
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Paper Abstract

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.

Paper Details

Date Published: 5 January 1989
PDF: 11 pages
Proc. SPIE 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation, (5 January 1989); doi: 10.1117/12.948956
Show Author Affiliations
Giulio Capocaccia, University of Genoa (Italy)
Alberto Damasio, University of Genoa (Italy)
Carlo S. Regazzoni, University of Genoa (Italy)
Gianni Vernazza, University of Genoa (Italy)

Published in SPIE Proceedings Vol. 1003:
Sensor Fusion: Spatial Reasoning and Scene Interpretation
Paul S. Schenker, Editor(s)

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