Spectral Detection of Human Targets

A three-stage algorithm suite is proposed for a specific human target detection scenario, where a visible/near infrared hyperspectral (HS) sample is assumed to be available as the only cue from a reference image frame. The suite first applies a biometric based human skin detector to focus the attention of the search. Using as reference all of the bands in the spectral cue, the suite follows with a Bayesian Lasso inference stage designed to isolate pixels representing the specific material type cued by the user and worn by the human target (e.g., hat, jacket). In essence, the search focuses on testing material types near skin pixels. The third stage imposes an additional constraint through RGB color quantization and distance metric checking, limiting even further the search for material types in the scene having visible color similar to the target color. Using the proposed cumulative evidence strategy produced some encouraging range-invariant results on real HS imagery, dramatically reducing to zero the false alarm rate on the example dataset. These results were in contrast to the results independently produced by each one of the suite’s stages, as the spatial areas of each stage’s high false alarm outcome were mutually exclusive in the imagery. These conclusions also apply to results produced by other standard methods, in particular the kernel SVDD (support vector data description) and Matched Filter, as shown in this paper.