Adaptive camouflage of moving targets

Abstract

Targets that are well camouflaged under static conditions are often easily detected as soon as they start moving. We investigated and evaluated ways to design camouflage that dynamically adapts to the background and conceals the target while taking the variation in potential viewing directions into account. In a human observer experiment recorded imagery was used to simulate moving (either walking or running) and static soldiers, equipped with different types of camouflage patterns and viewed from different directions. Participants were instructed to search for the soldier and to make a speeded response as soon as they detected the soldier. Mean correct search times and mean detection probability were compared between soldiers in standard (Netherlands) Woodland uniform, in static camouflage (adapted to the local background) and in dynamically adapting camouflage. We investigated the effects of background type and variability on detection performance by varying the soldiers’ environment (like bushland, and urban). In general, performance was worse for dynamic soldiers compared to static soldiers, confirming the notion that motion breaks camouflage. Furthermore, camouflage performance of the static adaptive camouflage condition was generally much better than for the standard Woodland camouflage. Also, camouflage performance was found to depend on the background. When moving across a highly variable (heterogenous) background, dynamic camouflage turned out to be especially beneficial (i.e., performance was better in a bush environment than in an urban environment). Interestingly, our dynamic camouflage design was outperformed a method which simply displays the ‘exact’ background on the camouflage suit, since it is better capable of taking the variability in viewing directions into account. By combining new adaptive camouflage technologies with dynamic adaptive camouflage designs such as the one presented here it may become possible to prevent detection of moving targets in the (near) future.