HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments

A new method (HoGG) for human detection based on Gabor filters and Histograms of Oriented Gradients is presented in this paper. The effect of Gabor preprocessing is analyzed in detail, in particular the improvement experienced by the image's information and the influence exerted over the extracted feature. To compare the performance of the proposed method, several alternative algorithms for human detection have been considered. In order to evaluate these techniques in non-controlled environments, a collection of standard databases, well known in the surveillance research community, has been used: PETS 2006, PETS 2007, PETS 2009 and CAVIAR. An exhaustive test design has been built based on two complementary evaluations: an evaluation oriented to counting people and a novel evaluation oriented to identification. Moreover, with the purpose of studying the performance of the Gabor-based preprocessing, a test adding Gabor filters to other local feature extraction methods, such as Steerable filters and the SIFT method, has been implemented. The HoGG method has achieved a good performance regardless of the difficulty of the images (occlusions, overlapping, carrying baggage, etc.). The proposed method has surpassed the alternative techniques in most of the analyzed situations. When the Gabor preprocessing is introduced into other local feature extraction methods, they achieve a better detection of the relevant information by enhancing the human shape. The results show that using Gabor preprocessing in techniques based on features like orientation or magnitude of gradient improve their performance. Given the excellent results obtained by HoGG at the identification-oriented evaluation, the method presented in this paper should be taken into account in the future design of intelligent surveillance systems. (C) 2012 Elsevier B.V. All rights reserved.