Automatic video surveillance in uncontrolled outdoor settings is a very challenging computer vision task. Nearly infinite variability of the environmental factors and the open-ended goals of many surveillance problems conspire to create situations where even the most advanced detection, tracking and recognition algorithms falter. While the common academic response to such challenges is to develop new, more powerful algorithms capable of handling a broader range of conditions with acceptable performance, this course of action is sometimes not appropriate from the industrial-commercial point of view. Sometimes systems must be deployed sooner than would allow for the development cycle of complex new algorithms, and must be more robust than most such algorithms can be expected to be on short notice. Under those circumstances, one may look toward better data quality as one means of improving performance while remaining close to the existing state-of-the-art in algorithmic technology. This is often the motivation for deployment of multimodal surveillance systems in the real world. This presentation constitutes a brief foray into design issues for visible-thermal systems, with emphasis on biometric surveillance.

We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first-order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first-order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed. A variety of experimental results are presented to support the performance of the new method.

A key problem for face recognition has been accurate identification under variable illumination conditions. Conventional video cameras sense reflected light so that image grayvalues are a product of both intrinsic skin reflectivity and external incident illumination, thus obfuscating the intrinsic reflectivity of skin. Thermal emission from skin, on the other hand, is an intrinsic measurement that can be isolated from external illumination. We examine the invariance of Long-Wave InfraRed (LWIR) imagery with respect to different illumination conditions from the viewpoint of performance comparisons of two well-known face recognition algorithms applied to LWIR and visible imagery. We develop rigourous data collection protocols that formalize face recognition analysis for computer vision in the thermal IR.

We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed and a discussion is given as to why our algorithm is optimal and outperforms them. A variety of experimental results are presented.