Visual object detection by parts-based modeling using extended histogram of gradients

In this paper, we present a parts-based modeling framework using Extended Histogram of Gradients (ExHoG) for object detection. Visual object detection is a challenging issue in computer vision where objects need to be detected in varying illumination and contrast environments. Furthermore, objects belonging to the same class exhibit large intra-class variations. Here, we propose using ExHoG with the discriminatively trained deformable part models of Felzenszwalb et. al. [1]. This framework is based on mixtures of multiscale deformable part models. ExHoG is a novel feature proposed earlier for the purpose of human detection and has shown promising results against other state-of-the-art approaches. The proposed approach is tested on INRIA Human data set and the PASCAL VOC 2007 data set. Results demonstrate superior performance on INRIA compared to existing state-of-the-art approaches and improved performance on PASCAL VOC 2007.