Proceedings Article | 2 May 2008
KEYWORDS: Databases, Defense and security, Detection and tracking algorithms, Automatic target recognition, Roads, Target detection, Infrared imaging, Video, Image processing, Infrared radiation
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.
