Background Subtraction for Moving Object Detection in RGBD Data: A Survey
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 suffi...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-05-01
|
Series: | Journal of Imaging |
Subjects: | |
Online Access: | http://www.mdpi.com/2313-433X/4/5/71 |
id |
doaj-e75d39c44bb649e38719434ee2df4e59 |
---|---|
record_format |
Article |
spelling |
doaj-e75d39c44bb649e38719434ee2df4e592020-11-24T21:49:48ZengMDPI AGJournal of Imaging2313-433X2018-05-01457110.3390/jimaging4050071jimaging4050071Background Subtraction for Moving Object Detection in RGBD Data: A SurveyLucia Maddalena0Alfredo Petrosino1National Research Council, Institute for High-Performance Computing and Networking, 80131 Naples, ItalyDepartment of Science and Technology, University of Naples Parthenope, 80143 Naples, ItalyThe 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.http://www.mdpi.com/2313-433X/4/5/71background subtractioncolor and depth dataRGBD |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lucia Maddalena Alfredo Petrosino |
spellingShingle |
Lucia Maddalena Alfredo Petrosino Background Subtraction for Moving Object Detection in RGBD Data: A Survey Journal of Imaging background subtraction color and depth data RGBD |
author_facet |
Lucia Maddalena Alfredo Petrosino |
author_sort |
Lucia Maddalena |
title |
Background Subtraction for Moving Object Detection in RGBD Data: A Survey |
title_short |
Background Subtraction for Moving Object Detection in RGBD Data: A Survey |
title_full |
Background Subtraction for Moving Object Detection in RGBD Data: A Survey |
title_fullStr |
Background Subtraction for Moving Object Detection in RGBD Data: A Survey |
title_full_unstemmed |
Background Subtraction for Moving Object Detection in RGBD Data: A Survey |
title_sort |
background subtraction for moving object detection in rgbd data: a survey |
publisher |
MDPI AG |
series |
Journal of Imaging |
issn |
2313-433X |
publishDate |
2018-05-01 |
description |
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. |
topic |
background subtraction color and depth data RGBD |
url |
http://www.mdpi.com/2313-433X/4/5/71 |
work_keys_str_mv |
AT luciamaddalena backgroundsubtractionformovingobjectdetectioninrgbddataasurvey AT alfredopetrosino backgroundsubtractionformovingobjectdetectioninrgbddataasurvey |
_version_ |
1725887289749405696 |