Reliable and realistic methods for assessment of thermal infrared signature properties for military purposes are important. With a basis in ongoing developments of imaging technologies, especially towards mass markets, including small handheld cameras or automotive sensors, thermal infrared sensors are expected to pose an increasing detection threat in the future. In this paper, we present a field-based approach that evaluates thermal contrast of camouflage nets, as well as mobile camouflage systems. In the proposed method, relative differences in thermal behavior between target and background are evaluated in a controlled manner in an outdoor environment over extended periods of ten days or more. The camouflage materials under test are mounted identically, in operationally realistic environments, and recorded with a thermal sensor at an image rate of 6 images per hour. Hence, thermal contrast values between each target and selected parts of the scene background are obtained during a full 24 hour period of time. Weather data are collected along with the thermal image data. In the subsequent analysis, average thermal contrasts between targets and selected backgrounds are calculated for certain well-defined time slots, such as night, day and transition between day and night. Only time slots that satisfy weather conditions requirements are analyzed, as changing weather is expected to affect the thermal response to camouflage systems. We believe the proposed method is a good compromise between controlled lab-tests, which are hampered by their lack of transfer value to thermal behavior in theatre, and field measurements during operations, where reproducibility of data can be low.
Military textiles with camouflage pattern are an important part of the protection measures for soldiers. Military operational environments differ a lot depending on climate and vegetation. This requires very different camouflage pattern to achieve good protection. To find the best performing pattern for given environments we have in earlier evaluations mainly applied observer trials as evaluation method. In these camouflage evaluation test human observers were asked to search for targets (in natural settings) presented on a high resolution PC screen, and the corresponding detection times were recorded. Another possibility is to base the evaluation on simulations. CAMAELEON is a licensed tool that ranks camouflaged targets by their similarity with local backgrounds. The similarity is estimated through the parameters local contrast, orientation of structures in the pattern and spatial frequency, by mimicking the response and signal processing in the visual cortex of the human eye. Simulations have a number of advantages over observer trials, for example, that they are more flexible, cheaper, and faster. Applying these two methods to the same images of camouflaged targets we found that CAMAELEON simulation results didn’t match observer trial results for targets with disruptive patterns. This finding now calls for follow up studies in order to learn more about the advantages and pitfalls of CAMAELEON. During recent observer trials we studied new camouflage patterns and the effect of additional equipment, such as combat vests. In this paper we will present the results from a study comparing evaluation results of human based observer trials and CAMAELEON.
KEYWORDS: Mathematical modeling, Camouflage, Head, Target detection, Visualization, Image processing, Human vision and color perception, Sensors, 3D acquisition, Tolerancing
We present results from an observer based photosimulation study of generic camouflage patterns, intended for military uniforms, where three near-identical patterns have been compared. All the patterns were prepared with similar effective color, but were different in how the individual pattern patches were distributed throughout the target. We did this in order to test if high contrast (black) patches along the outline of the target would enhance the survivability when exposed to human observers. In the recent years it has been shown that disruptive coloration in the form of high contrast patches are capable of disturbing an observer by creating false edges of the target and consequently enhance target survivability. This effect has been shown in different forms in the Animal Kingdom, but not to the same extent in camouflaged military targets. The three patterns in this study were i) with no disruptive preference, ii) with a disruptive patch along the outline of the head and iii) with a disruptive patch on the outline of one of the shoulders. We used a high number of human observers to assess the three targets in 16 natural (woodland) backgrounds by showing images of one of the targets at the time on a high definition pc screen. We found that the two patterns that were thought to have a minor disruptive preference to the remaining pattern were more difficult to detect in some (though not all) of the 16 scenes and were also better in overall performance when all the scenes were accounted for.
KEYWORDS: Target detection, Camouflage, Head, Visualization, Eye, Spatial frequencies, Visual process modeling, Visual system, Human vision and color perception, Photography
Evaluation of signature properties of military equipment is very important. It is crucial to apply the proper method out of many possible approaches, based on amongst others ranking by probability of detection, detection time, and distance to target, which have been carried out by various countries. In this paper we present results from camouflage pattern assessments utilising two different approaches, based on human observers (detection time) and simulations (CAMAELEON). CAMAELEON ranks camouflaged targets by their local contrast, orientation and spatial frequency, mimicking the human eye’s response, and is a rapid and low cost method for signature assessment. In our camouflage tests, human observers were asked to search for targets (in a natural setting) presented on a high resolution pc screen, and the corresponding detection times were recorded. In our study we find a good correspondence between the camouflage properties of the targets in most of our unique tests (scenes), but in some particular cases there is an interesting deviation. Two similar camouflage patterns (both were random samples of the pattern) were tested, and it seemed that the results depended on the way the pattern is attached to the test subject. More precisely, it may seem that high-contrast coloured patches of the pattern in the target outline were significantly different detected by humans compared to CAMAELEON. In this paper we discuss this deviation in the two signature evaluation methods and look at potential risks.
Reliable, low-cost and simple methods for assessment of signature properties for military purposes are very important. In this paper we present such an approach that uses human observers in a search by photo assessment of signature properties of generic test targets. The method was carried out by logging a large number of detection times of targets recorded in relevant terrain backgrounds. The detection times were harvested by using human observers searching for targets in scene images shown by a high definition pc screen. All targets were identically located in each “search image”, allowing relative comparisons (and not just rank by order) of targets. To avoid biased detections, each observer only searched for one target per scene. Statistical analyses were carried out for the detection times data. Analysis of variance was chosen if detection times distribution associated with all targets satisfied normality, and non-parametric tests, such as Wilcoxon’s rank test, if otherwise. The new methodology allows assessment of signature properties in a reproducible, rapid and reliable setting. Such assessments are very complex as they must sort out what is of relevance in a signature test, but not loose information of value. We believe that choosing detection times as the primary variable for a comparison of signature properties, allows a careful and necessary inspection of observer data as the variable is continuous rather than discrete. Our method thus stands in opposition to approaches based on detections by subsequent, stepwise reductions in distance to target, or based on probability of detection.
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