Depth sensor based human detection for indoor surveillance

Human detection is a popular topic and difficult problem in surveillance. This paper presents a research on human detection in complex indoor space utilizing a depth sensor. In recent years, target detection methods based on RGB-D data mainly include background learning, and feature detection operator. The former method depends on the initial background knowledge obtained from the first couple of frames in the video, and the length of frames decides detection quality. The latter method is more time intensive, and insufficient training samples will influence the detection result. Thus, in this paper we analyze the complex scene features thoroughly and integrate the color and depth information, proposing a RGBD+ViBe foreground extraction method. Based on the extraction outcome of the foreground, this research utilizes the 3D Mean-Shift method combined with depth constraints to handle multi-person partial occlusion problems. The experiment results indicate that the proposed RGBD+ViBe method outperforms the methods which only consider color or depth information, as well as the RGBD+MoG method. Furthermore, the proposed 3D Mean-Shift method achieves nearly 90% accuracy in multi-person detection result, and the false rate is merely 5%; while the accuracy of HOG, HOD and Comb-HOD methods are less than 75% and the false rate is around 10%.