Person detection in surveillance environment with HoGG: Gabor filters and Histogram of Oriented Gradient

In this paper, a novel method (HoGG) for human detection in non-controlled surveillance environments based on Gabor filters and Histograms of Oriented Gradients is proposed. Complete performance results of several alternative algorithms for human detection are presented. In order to evaluate these methods in non-controlled situations, a set of standard video surveillance databases (PETS 2006, PETS 2007, PETS 2009 and CAVIAR) as well as the classical (in people detection area) INRIA database are used. An exhaustive test design has been built based on two complementary evaluations: an evaluation oriented to count persons and a novel evaluation oriented to identification. The proposed method has surpassed the alternative methods in most of the analyzed situations, this is a relevant improvement of the widely used HoG algorithm.