Color Contribution to Part-Based Person Detection in Different Types of Scenarios

Abstract

Camera-based person detection is of paramount interest due to its potential applications. The task is difficult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted sufficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system. We will compute the HOG on top of OPP, then we train and test the part-based human classifier by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the benefits of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution.