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J. Imaging 2018, 4(5), 71; https://doi.org/10.3390/jimaging4050071

Background Subtraction for Moving Object Detection in RGBD Data: A Survey

1
National Research Council, Institute for High-Performance Computing and Networking, 80131 Naples, Italy
2
Department of Science and Technology, University of Naples Parthenope, 80143 Naples, Italy
*
Author to whom correspondence should be addressed.
Received: 16 April 2018 / Revised: 7 May 2018 / Accepted: 9 May 2018 / Published: 16 May 2018
(This article belongs to the Special Issue Detection of Moving Objects)
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Abstract

The paper provides a specific perspective view on background subtraction for moving object detection, as a building block for many computer vision applications, being the first relevant step for subsequent recognition, classification, and activity analysis tasks. Since color information is not sufficient for dealing with problems like light switches or local gradual changes of illumination, shadows cast by the foreground objects, and color camouflage, new information needs to be caught to deal with these issues. Depth synchronized information acquired by low-cost RGBD sensors is considered in this paper to give evidence about which issues can be solved, but also to highlight new challenges and design opportunities in several applications and research areas. View Full-Text
Keywords: background subtraction; color and depth data; RGBD background subtraction; color and depth data; RGBD
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Maddalena, L.; Petrosino, A. Background Subtraction for Moving Object Detection in RGBD Data: A Survey. J. Imaging 2018, 4, 71.

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