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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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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 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.
Keywords: object detection; background subtraction; video surveillance; Kinect sensor fusion
College: Faculty of Science and Engineering
Issue: 6
Start Page: 1177

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