  We address the problem of people detection in RGB-D data where we leverage
depth information to develop a region-of-interest (ROI) selection method that
provides proposals to two color and depth CNNs. To combine the detections
produced by the two CNNs, we propose a novel fusion approach based on the
characteristics of depth images. We also present a new depth-encoding scheme,
which not only encodes depth images into three channels but also enhances the
information for classification. We conduct experiments on a publicly available
RGB-D people dataset and show that our approach outperforms the baseline models
that only use RGB data.
