A Moving Object Detection Method Combining Color and Depth data

HU Tao; ZHU Xinyan; GUO Wei; ZHANG Faming

doi:10.13203/j.whugis20160535

Volume 44 Issue 2

Feb. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(2): 276-282. > DOI: 10.13203/j.whugis20160535

HU Tao, ZHU Xinyan, GUO Wei, ZHANG Faming. A Moving Object Detection Method Combining Color and Depth data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(2): 276-282. DOI: 10.13203/j.whugis20160535

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A Moving Object Detection Method Combining Color and Depth data

HU Tao^{1, 2, 3, 4,},
ZHU Xinyan^{1, 2},
GUO Wei^{1, 2, ,},
ZHANG Faming¹

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
3.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
4.
School of Library and Information Science, Kent State University, Kent 44240, Ohio, USA

Funds:

The National Natural Science Foundation of China 41301517

the National Key Research and Development Program 2016YFB0502204

the Fundamental Research Funds for the Central Universities 413000010

Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (16)03

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Author Bio:
HU Tao, PhD, al fellow, specializes in urban behavior analysis. E-mail: taohu07@hotmail.com
Corresponding author:
GUO Wei, PhD, associate professor. E-mail: guowei-lmars@whu.edu.cn
Received Date: April 04, 2017
Published Date: February 04, 2019

Graphical Abstract

Abstract

Abstract

Pedestrian detection is a hot topic and difficult problem in areas like computer vision and intelligent traffic. This paper researches on pedestrian detection in complex indoor space using depth sensors. In recent years, target detection methods based on RGB-Depth(RGBD) data mainly include background learning method and feature detecting operator method. However, the former method depends on the background knowledge of first tens of frames, and the number of frames decides the final detection accuracy. The latter takes plenty of time for computing, and being lacks of training samples may influence the detection result. Thus, this paper analyzes the complex scene features and integrates the color and depth information, and proposes a RGBD+ViBe (visual background extractor) background elimination method. The experiment results indicate that the detection accuracy of the proposed method is higher than the methods which only consider color or depth information and the RGBD+MoG method in foreground extraction.
- depth sensor,
- RGBD,
- moving target extraction,
- people detection,
- ViBe

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References

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