A common surveillance problem is the automatic detection of objects concealed under clothing and the identification of those carrying them. As many 2D methods rely on texture information, the application of patterned clothing can be used to camouflage features that may provide a clue as to the shape of the object hidden beneath.
Photometric stereo (PS) is a 3D surface reconstruction technique utilising several images of an object, lit from multiple directions, a particular advantage of which is that it reliably separates textural elements, such as printed patterns, from physical shape offering many possibilities for concealed object detection.
The success of such a technique is primarily dependent on the ability to artificially illuminate the subject considerably more brightly than the ambient lighting. At night, this is readily plausible; and longer wavelength, near-infrared (nIR) lighting allows us to capture the images covertly. However in daytime, sunlight can prevent sufficient illumination of the subject to calculate the surface image, especially at long range.
Certain wavelengths of light are attenuated by airborne moisture considerably more than others. By using a wavelength of light that is heavily attenuated by the atmosphere, in combination with a narrow bandpass filter, we show that it is possible to provide sufficient lighting contrast to perform PS over much longer distances than in previous work.
We examine the 940nm wavelength, which falls within one of these spectral regions and evaluate sensor technology equipped with a “black silicon” CMOS, offering extreme light sensitivity, against cameras using traditional silicon sensors, with application to long distance surface reconstruction using PS.
Having shown that we can produce reconstructions of considerably better quality than those from traditional cameras, we present several methods for the reliable detection of concealed objects and recognition of faces, using the high level of surface detail that PS can provide.