dc.contributor.author |
Mattheus, J
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dc.contributor.author |
Grobler, H
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|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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dc.date.accessioned |
2021-02-09T11:07:55Z |
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dc.date.available |
2021-02-09T11:07:55Z |
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dc.date.issued |
2020-11 |
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dc.identifier.citation |
Mattheus, J., Grobler, H. & Abu-Mahfouz, A.M. 2020. A review of motion segmentation: Approaches and major challenges. http://hdl.handle.net/10204/11740 . |
en_ZA |
dc.identifier.uri |
http://hdl.handle.net/10204/11740
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|
dc.description.abstract |
Motion segmentation has applications in, amongst others, robotics, traffic monitoring, sports analysis, inspection, video surveillance, compression, and video indexing. However, the performance of most methods is limited compared to human capabilities. Based on extensive literature the following challenges remain: occlusions, temporary stopping, missing data, and segmenting multiple objects. In this paper, several popular and state-of-the-art methods were reviewed, with the focus on the most important attributes. These methods were classified according to the main approach taken, namely Image Difference, Optical Flow, Wavelet, Statistical, Layers, Manifold Clustering, Template Matching, and Deep Learning. The investigated methods are compared and major research challenges are highlighted. Based on the review, improvements are identified as a basis for future research. |
en_US |
dc.format |
Full text |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.spu.ac.za/index.php/ieee-imitec-2020/ |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/9334076 |
en_US |
dc.relation.uri |
DOI: 10.1109/IMITEC50163.2020.9334076 |
en_US |
dc.relation.uri |
978-1-7281-9520-9 |
en_US |
dc.relation.uri |
978-1-7281-9521-6 |
en_US |
dc.source |
International Multidisciplinary Information Technology and Engineering Conference, Kimberley, South Africa, 25-27 November 2020 |
en_US |
dc.subject |
3D scene analysis |
en_US |
dc.subject |
Articulated |
en_US |
dc.subject |
Computer vision |
en_US |
dc.subject |
Factorization method |
en_US |
dc.subject |
Manifold clustering |
en_US |
dc.subject |
Motion analysis |
en_US |
dc.subject |
Motion segmentation |
en_US |
dc.subject |
Non-rigid |
en_US |
dc.title |
A review of motion segmentation: Approaches and major challenges |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
8pp |
en_US |
dc.description.note |
Copyright: 2020 IEEE. This is the pre-print version of the work. |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
|
dc.description.impactarea |
EDTRC Management |
en_US |
dc.identifier.apacitation |
Mattheus, J., Grobler, H., & Abu-Mahfouz, A. M. (2020). A review of motion segmentation: Approaches and major challenges. http://hdl.handle.net/10204/11740 |
en_ZA |
dc.identifier.chicagocitation |
Mattheus, J, H Grobler, and Adnan MI Abu-Mahfouz. "A review of motion segmentation: Approaches and major challenges." <i>International Multidisciplinary Information Technology and Engineering Conference, Kimberley, South Africa, 25-27 November 2020</i> (2020): http://hdl.handle.net/10204/11740 |
en_ZA |
dc.identifier.vancouvercitation |
Mattheus J, Grobler H, Abu-Mahfouz AM, A review of motion segmentation: Approaches and major challenges; 2020. http://hdl.handle.net/10204/11740 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Mattheus, J
AU - Grobler, H
AU - Abu-Mahfouz, Adnan MI
AB - Motion segmentation has applications in, amongst others, robotics, traffic monitoring, sports analysis, inspection, video surveillance, compression, and video indexing. However, the performance of most methods is limited compared to human capabilities. Based on extensive literature the following challenges remain: occlusions, temporary stopping, missing data, and segmenting multiple objects. In this paper, several popular and state-of-the-art methods were reviewed, with the focus on the most important attributes. These methods were classified according to the main approach taken, namely Image Difference, Optical Flow, Wavelet, Statistical, Layers, Manifold Clustering, Template Matching, and Deep Learning. The investigated methods are compared and major research challenges are highlighted. Based on the review, improvements are identified as a basis for future research.
DA - 2020-11
DB - ResearchSpace
DP - CSIR
J1 - International Multidisciplinary Information Technology and Engineering Conference, Kimberley, South Africa, 25-27 November 2020
KW - 3D scene analysis
KW - Articulated
KW - Computer vision
KW - Factorization method
KW - Manifold clustering
KW - Motion analysis
KW - Motion segmentation
KW - Non-rigid
LK - https://researchspace.csir.co.za
PY - 2020
T1 - A review of motion segmentation: Approaches and major challenges
TI - A review of motion segmentation: Approaches and major challenges
UR - http://hdl.handle.net/10204/11740
ER - |
en_ZA |
dc.identifier.worklist |
24113 |
|