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Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm

Xiaoqin Zhou, Xiaofeng Liu, Aimin Jiang, Bin Yan, Chenguang Yang

Sensors, Volume: 17, Issue: 6, Start page: 1177

Swansea University Author: Chenguang Yang

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DOI (Published version): 10.3390/s17051177

Abstract

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in three-dimensional space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segme...

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Published in: Sensors
ISSN: 1424-8220
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa34047
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first_indexed 2017-05-31T14:14:50Z
last_indexed 2018-02-09T05:23:48Z
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spelling 2017-07-31T15:05:08.5317177 v2 34047 2017-05-31 Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm d2a5024448bfac00a9b3890a8404380b Chenguang Yang Chenguang Yang true false 2017-05-31 EEN Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in three-dimensional space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D (Red-Green-Blue and Depth) algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency. Journal Article Sensors 17 6 1177 1424-8220 object detection; background subtraction; video surveillance; Kinect sensor fusion 31 12 2017 2017-12-31 10.3390/s17051177 COLLEGE NANME Engineering COLLEGE CODE EEN Swansea University 2017-07-31T15:05:08.5317177 2017-05-31T09:26:40.1386496 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Xiaoqin Zhou 1 Xiaofeng Liu 2 Aimin Jiang 3 Bin Yan 4 Chenguang Yang 5 0034047-31052017092858.pdf zhou2017.pdf 2017-05-31T09:28:58.7170000 Output 6360746 application/pdf Version of Record true 2017-05-31T00:00:00.0000000 true eng
title Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm
spellingShingle Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm
Chenguang Yang
title_short Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm
title_full Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm
title_fullStr Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm
title_full_unstemmed Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm
title_sort Improving Video Segmentation by Fusing Depth Cues and the Visual Background Extractor (ViBe) Algorithm
author_id_str_mv d2a5024448bfac00a9b3890a8404380b
author_id_fullname_str_mv d2a5024448bfac00a9b3890a8404380b_***_Chenguang Yang
author Chenguang Yang
author2 Xiaoqin Zhou
Xiaofeng Liu
Aimin Jiang
Bin Yan
Chenguang Yang
format Journal article
container_title Sensors
container_volume 17
container_issue 6
container_start_page 1177
publishDate 2017
institution Swansea University
issn 1424-8220
doi_str_mv 10.3390/s17051177
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
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description Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in three-dimensional space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D (Red-Green-Blue and Depth) algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.
published_date 2017-12-31T03:42:15Z
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score 11.014537

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