Fast Human Detection In Rgb-D Images Based On Color-Depth Joint Feature Learning

Human detection in RGB-D images is an important yet very challenging task in computer vision. In this paper, we propose a novel human detection approach in RGB-D images, which integrates ROI (region-of-interest) generation, depth-size relationship estimation and a human detector. Our approach has the following advantages: 1) ROI generation and depth size relationship estimation take full advantage of color and depth information to fast reject about 70% negative samples while maintaining a high recall rate; 2) the cascade-structured human detector can seamlessly concatenate features extracted from both color and depth images; and 3) our method can detect human at a speed of more than 30 fps on 640 x 480 images on a single laptop CPU without any GPU acceleration. Experiments on challenging public datasets demonstrate the effectiveness of our method.