TY - JOUR
T1 - Active Perception for Foreground Segmentation: An RGB-D Data-Based Background Modeling Method
AU - Sun, Yuxiang
AU - Liu, Ming
AU - Meng, Max Q.H.
N1 - Funding Information:
Manuscript received August 8, 2018; revised November 19, 2018; accepted January 7, 2019. Date of publication February 13, 2019; date of current version October 4, 2019. This paper was recommended for publication by Associate Editor H. Liu and Editor H. Liu upon evaluation of the reviewers’ comments. This work was supported by the Shenzhen Science and Technology Innovation Project JCYJ20160428154842603, JCYJ20170413161616163, the Hong Kong Research Grant Council (RGC) Project 11210017, 16212815, 21202816, 14205914, 14200618, the ITC ITF Project ITS/236/15, the National Natural Science Foundation of China Project U1713211. (Corresponding authors: Ming Liu; Max Q.-H. Meng.) Y. Sun and M. Liu are with the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong (e-mail: [email protected], [email protected]; eelium@ ust.hk).
Funding Information:
This work was supported by the Shenzhen Science and Technology Innovation Project JCYJ20160428154842603, JCYJ20170413161616163, the Hong Kong Research Grant Council (RGC) Project 11210017, 16212815, 21202816, 14205914, 14200618, the ITC ITF Project ITS/236/15, the National Natural Science Foundation of China Project U1713211.*%blankline%*
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - Active perception
KW - background modeling
KW - foreground segmentation
KW - RGB-D camera
UR - http://www.scopus.com/inward/record.url?scp=85065675120&partnerID=8YFLogxK
U2 - 10.1109/TASE.2019.2893414
DO - 10.1109/TASE.2019.2893414
M3 - Journal article
AN - SCOPUS:85065675120
SN - 1545-5955
VL - 16
SP - 1596
EP - 1609
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
IS - 4
M1 - 8641464
ER -