Fast Motion Object Detection Algorithm Using Complementary Depth Image on an RGB-D Camera

Abstract

Stereo vision has become a popular topic in recent years, especially in-depth images from stereo vision. Depth information can be extracted either from a dual camera or RGB-D camera. In image processing, the realization of object detection is only based on the color information or depth images separately; however, both have advantages and disadvantages. Therefore, many researchers have combined them together to achieve better results. A new fast motion object-detection algorithm is presented based on the complementary depth images and color information, which is able to detect motion objects without background noise. The experiment results show that the proposed fast object detection algorithm can achieve 84.4% of the segmentation accuracy rate on average with a 45 FPS computation speed on an embedded platform.