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

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.

I. Introduction

Recently, stereo vision has been used in surveillance systems to improve object recognition and tracking efficiency performance [1], [2]. Stereo vision can provide depth and color information for advanced image processing. Intrinsically different than RGB information, pixels in a depth map essentially represent 3-D space using stereo vision; hence, depth video represents the variation of the space information. Advances and developments in computer vision now can obtain stereo vision from either a dual camera system [3]–[10] or an RGB-D camera [11]–[14]. A dual camera system uses two video camera inputs to capture images through stereo matching then calculate the distance between the object and the camera. Calibration in a dual camera system is necessary before calculating the disparity map; however, the computational complexity of disparity match is typically difficult. Still, a major advantage is that the depth range is adjustable. On the other hand, an RGB-D system is composed of an RGB camera and a depth sensor, such as a Kinect or Asus Xtion Pro [15]. The biggest advantage of this system is that it is more convenient for acquiring depth information from a real-time video stream compared to the dual camera system. However, the depth information is noisy due to hardware drawbacks of a time-of-flight infrared-based depth sensor, and it can’t be used in outdoor environments.