Real-time human detection with depth camera via a physical radius-depth detector and a CNN descriptor

Real-time human detection is important for a wide range of applications. In this paper, a two-staged method has been developed for real-time human detection in cluttered and dynamic environments with depth data. We start with generating a set of possible human head-tops to ensure all human locations are included. To this end, a novel physical radius-depth (PRD) detector is proposed to quickly detect human candidates. The second stage applies a convolutional neural network (CNN), aiming at extracting feature of human upper body automatically instead of hand-crafting, and then on the basis of CNN feature genuine human candidates are preserved while false ones are filtered out. Experiment results on four publicly available datasets, including a dataset under weak illumination or even total darkness, show that the proposed method can reliably detect human in RGB-D videos in real time without GPU acceleration, and yields higher accuracy than the compared state-of-the-art approaches.