Active Perception for Foreground Segmentation: An RGB-D Data-Based Background Modeling Method

Abstract

Foreground moving object segmentation is a fundamental problem in many computer vision applications. As a solution for foreground segmentation, background modeling has been intensively studied over past years and many effective algorithms have been developed. However, accurate foreground segmentation is still a difficult problem. Currently, most of the algorithms work solely within the color space, in which the segmentation performance is prone to be degraded by a multitude of challenges, such as illumination changes, shadows, automatic camera adjustments, and color camouflage. RGB-D cameras are active visual sensors that provide depth measurements along with color images. We present in this paper an innovative background modeling method by using both the color and depth information from an RGB-D camera. The proposed method is evaluated using a public RGB-D data set. Various experiments confirm that our method is able to achieve superior performance compared with existing well-known methods. Note to Practitioners —This paper investigates background modeling for foreground segmentation with active perception. Recent RGB-D cameras that leverage the active perception technology have advanced many computer vision algorithms. In this paper, we develop a background modeling method to achieve superior performance by using an RGB-D camera instead of a color camera. Due to the use of the active sensing technology, the proposed method is characterized by its robustness to common challenges. Our method could be used for improving existing infrastructures, such as visual surveillance systems for parking spaces. Moreover, the simple design of our method allows it to be easily deployed on various computing platforms, which facilitates many practical applications that usually require embedded computing devices. However, our method cannot run real timely at the current status. We believe that it can be further improved using parallel programming techniques to meet the real-time requirement.