Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Sparse subspace clustering-based motion segmentation with complete occlusion handling
Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2021
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613523610173440 |
|---|---|
| access_status_str | Open Access |
| author2 | Grobler, H. |
| author_browse | Grobler, H. |
| author_facet | Grobler, H. |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/81250 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:30.383Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/81250 Sparse subspace clustering-based motion segmentation with complete occlusion handling Grobler, H. mattheus.jana@gmail.com Abu-Mahfouz, Adnan M. Mattheus, Jana UCTD Motion segmentation Motion analysis Sparse subspace clustering Manifold clustering Computer vision Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021. Motion segmentation is part of the computer vision field and aims to find the moving parts in a video sequence. It is used in applications such as autonomous driving, surveillance, robotics, human motion analysis, and video indexing. Since there are so many applications, motion segmentation is ill-defined and the research field is vast. Despite the advances in the research over the years, the existing methods are still far behind human capabilities. Problems such as changes in illumination, camera motion, noise, mixtures of motion, missing data, and occlusion remain challenges. Feature-based approaches have grown in popularity over the years, especially manifold clustering methods due to their strong mathematical foundation. Methods exploiting sparse and low-rank representations are often used since the dimensionality of the data is reduced while useful information regarding the motion segments is extracted. However, these methods are unable to effectively handle large and complete occlusions as well as missing data since they tend to fail when the amount of missing data becomes too large. An algorithm based on Sparse Subspace Clustering (SSC) has been proposed to address the issue of occlusions and missing data so that SSC can handle these cases with high accuracy. A frame-to-frame analysis was adopted as a pre-processing step to identify motion segments between consecutive frames, called inter-frame motion segments. The pre-processing step is called Multiple Split-And-Merge (MSAM), which is based on the classic top-down split-and-merge algorithm. Only points present in both frame pairs are segmented. This means that a point undergoing an occlusion is only assigned to a motion class when it has been visible for two consecutive frames after re-entering the camera view. Once all the inter-frame segments have been extracted, the results are combined in a single matrix and used as the input for the classic SSC algorithm. Therefore, SSC segments inter-frame motion segments rather than point trajectories. The resulting algorithm is referred to as MSAM-SSC. MSAM-SSC outperformed some of the most popular manifold clustering methods on the Hopkins155 and KT3DMoSeg datasets. It was also able to handle complete occlusions and 50% missing data sequences, as well as outliers. The algorithm can handle mixtures of motions and different numbers of motions. However, it was found that MSAM-SSC is more suited for traffic and articulate motion scenes which are often used in applications such as robotics, surveillance, and autonomous driving. For future work, the algorithm can be optimised to reduce the execution time so that it can be used for real-time applications. Additionally, the number of moving objects in the scene can be estimated to obtain a method that does not rely on prior knowledge. CSIR Electrical, Electronic and Computer Engineering MEng (Computer Engineering) Unrestricted 2021-08-13T06:32:30Z 2021-08-13T06:32:30Z 2021-09 2021 Dissertation * S2021 http://hdl.handle.net/2263/81250 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Motion segmentation Motion analysis Sparse subspace clustering Manifold clustering Computer vision Sparse subspace clustering-based motion segmentation with complete occlusion handling |
| title | Sparse subspace clustering-based motion segmentation with complete occlusion handling |
| title_full | Sparse subspace clustering-based motion segmentation with complete occlusion handling |
| title_fullStr | Sparse subspace clustering-based motion segmentation with complete occlusion handling |
| title_full_unstemmed | Sparse subspace clustering-based motion segmentation with complete occlusion handling |
| title_short | Sparse subspace clustering-based motion segmentation with complete occlusion handling |
| title_sort | sparse subspace clustering based motion segmentation with complete occlusion handling |
| topic | UCTD Motion segmentation Motion analysis Sparse subspace clustering Manifold clustering Computer vision |
| url | http://hdl.handle.net/2263/81250 |