A Moving Object Detection Method Combining Color and Depth data

Pedestrian detection is a hot topic and difficult problem in areas like computer vision and intelligent traffic. This paper researches on pedestrian detection in complex indoor space using depth sensors. In recent years, target detection methods based on RGB-Depth(RGBD) data mainly include background learning method and feature detecting operator method. However, the former method depends on the background knowledge of first tens of frames, and the number of frames decides the final detection accuracy. The latter takes plenty of time for computing, and being lacks of training samples may influence the detection result. Thus, this paper analyzes the complex scene features and integrates the color and depth information, and proposes a RGBD+ViBe (visual background extractor) background elimination method. The experiment results indicate that the detection accuracy of the proposed method is higher than the methods which only consider color or depth information and the RGBD+MoG method in foreground extraction.