ATR-based camouflage effectiveness evaluation of MUSTAFA targets

An international multisensor measurement campaign called "MUSTAFA" yielded many infrared image sequences of differently camouflaged targets. The image sequences were acquired by a helicopter sensor platform approaching the targets. The effectiveness of the various camouflage methods still has to be evaluated. Apart from observer experiments, FGAN/FOM and IITB pursue an ATR (Automatic Target Recognition) -based method for the automatic evaluation of the camouflage variants. The ATR approach consists basically of the detection component of an ATR for reconnaissance purposes in forward-looking infrared image sequences (FLIR). Given some flight and sensor parameters the algorithm can report detection hypothesizes together with a measure of confidence and the detection range for each hypothesis. Proceeding on the assumption that better camouflage yields late automatic detection of the corresponding target in approaching image sequences, the detection range output of the algorithm could be an additional criteria for camouflage evaluation. The paper presents some aspects of the reconnaissance detection algorithm, detection ranges for exemplary image sequences of the MUSTAFA data set, and future options: e.g. real-time operation in the sensor platform during the measurement campaign.