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Proceedings Paper

ROBIN: a platform for evaluating automatic target recognition algorithms: I. Overview of the project and presentation of the SAGEM DS competition
Author(s): D. Duclos; J. Lonnoy; Q. Guillerm; F. Jurie; S. Herbin; E. D'Angelo
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Paper Abstract

The last five years have seen a renewal of Automatic Target Recognition applications, mainly because of the latest advances in machine learning techniques. In this context, large collections of image datasets are essential for training algorithms as well as for their evaluation. Indeed, the recent proliferation of recognition algorithms, generally applied to slightly different problems, make their comparisons through clean evaluation campaigns necessary. The ROBIN project tries to fulfil these two needs by putting unclassified datasets, ground truths, competitions and metrics for the evaluation of ATR algorithms at the disposition of the scientific community. The scope of this project includes single and multi-class generic target detection and generic target recognition, in military and security contexts. From our knowledge, it is the first time that a database of this importance (several hundred thousands of visible and infrared hand annotated images) has been publicly released. Funded by the French Ministry of Defence (DGA) and by the French Ministry of Research, ROBIN is one of the ten Techno-vision projects. Techno-vision is a large and ambitious government initiative for building evaluation means for computer vision technologies, for various application contexts. ROBIN's consortium includes major companies and research centres involved in Computer Vision R&D in the field of defence: Bertin Technologies, CNES, ECA, DGA, EADS, INRIA, ONERA, MBDA, SAGEM, THALES. This paper, which first gives an overview of the whole project, is focused on one of ROBIN's key competitions, the SAGEM Defence Security database. This dataset contains more than eight hundred ground and aerial infrared images of six different vehicles in cluttered scenes including distracters. Two different sets of data are available for each target. The first set includes different views of each vehicle at close range in a "simple" background, and can be used to train algorithms. The second set contains many views of the same vehicle in different contexts and situations simulating operational scenarios.

Paper Details

Date Published: 2 May 2008
PDF: 12 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 696707 (2 May 2008); doi: 10.1117/12.777501
Show Author Affiliations
D. Duclos, SAGEM DS (France)
J. Lonnoy, SAGEM DS (France)
Q. Guillerm, SAGEM DS (France)
F. Jurie, CNRS, INRIA (France)
S. Herbin, ONERA (France)
E. D'Angelo, DGA/DET/ASC/EORD (France)


Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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