A new depth descriptor for pedestrian detection in RGB-D images

With the development of depth camera technology, it is feasible to get high quality color and depth images synchronously in real time. Thus, RGB-D-based applications are becoming more and more popular, such as pedestrian detection in RGB-D data. As the key point in this application is to search for better descriptions, in this paper we propose a new feature descriptor, Pyramid Depth Self-Similarities (PDSS), for depth images. It is based on the idea that depth information of people has high local self-similarities. The experiments, where RGB-D data is collected by a Kinect sensor, prove that PDSS is an effective complement to Histogram of Oriented Depth (HOD). Furthermore, the combination of Histogram of Oriented Gradients (HOG), HOD and PDSS improves the detection performance.