Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements
<p dir="ltr">Optical flow is the pattern of apparent motion of objects in a scene. The computation of optical flow is a critical component in numerous computer vision tasks such as object detection, visual object tracking, and activity recognition. Despite a lot of research, efficien...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513509721636864 |
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| author | Muhammad Wasim Nawaz (19325677) |
| author2 | Abdesselam Bouzerdoum (17900021) Muhammad Mahboob Ur Rahman (19325680) Ghulam Abbas (764241) Faizan Rashid (17541690) |
| author2_role | author author author author |
| author_facet | Muhammad Wasim Nawaz (19325677) Abdesselam Bouzerdoum (17900021) Muhammad Mahboob Ur Rahman (19325680) Ghulam Abbas (764241) Faizan Rashid (17541690) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Wasim Nawaz (19325677) Abdesselam Bouzerdoum (17900021) Muhammad Mahboob Ur Rahman (19325680) Ghulam Abbas (764241) Faizan Rashid (17541690) |
| dc.date.none.fl_str_mv | 2024-01-02T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3382818 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Dense_Optical_Flow_Estimation_Using_Sparse_Regularizers_From_Reduced_Measurements/26490946 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Computer vision and multimedia computation Energy minimization motion discontinuities optical flow sparse regularizers total variation Optical flow TV Estimation Anisotropic Computer vision Minimization Image motion analysis Energy management Motion control |
| dc.title.none.fl_str_mv | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Optical flow is the pattern of apparent motion of objects in a scene. The computation of optical flow is a critical component in numerous computer vision tasks such as object detection, visual object tracking, and activity recognition. Despite a lot of research, efficiently managing abrupt changes in motion remains a challenge in motion estimation. This paper proposes novel variational regularization methods to address this problem since they allow combining different mathematical concepts into a joint energy minimization framework. In this work, we incorporate concepts from signal sparsity into variational regularization for motion estimation. The proposed regularization uses robust ℓ1 norm, which promotes sparsity and handles motion discontinuities. By using this regularization, we promote the sparsity of the optical flow gradient. This sparsity helps recover a signal even with just a few measurements. We explore recovering optical flow from a limited set of linear measurements using this regularizer. Our findings show that leveraging the sparsity of the derivatives of optical flow reduces computational complexity and memory needs.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3382818" target="_blank">https://dx.doi.org/10.1109/access.2024.3382818</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1c855bd144df210fae5b96f6b19820cc |
| identifier_str_mv | 10.1109/access.2024.3382818 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26490946 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced MeasurementsMuhammad Wasim Nawaz (19325677)Abdesselam Bouzerdoum (17900021)Muhammad Mahboob Ur Rahman (19325680)Ghulam Abbas (764241)Faizan Rashid (17541690)Information and computing sciencesComputer vision and multimedia computationEnergy minimizationmotion discontinuitiesoptical flowsparse regularizerstotal variationOptical flowTVEstimationAnisotropicComputer visionMinimizationImage motion analysisEnergy managementMotion control<p dir="ltr">Optical flow is the pattern of apparent motion of objects in a scene. The computation of optical flow is a critical component in numerous computer vision tasks such as object detection, visual object tracking, and activity recognition. Despite a lot of research, efficiently managing abrupt changes in motion remains a challenge in motion estimation. This paper proposes novel variational regularization methods to address this problem since they allow combining different mathematical concepts into a joint energy minimization framework. In this work, we incorporate concepts from signal sparsity into variational regularization for motion estimation. The proposed regularization uses robust ℓ1 norm, which promotes sparsity and handles motion discontinuities. By using this regularization, we promote the sparsity of the optical flow gradient. This sparsity helps recover a signal even with just a few measurements. We explore recovering optical flow from a limited set of linear measurements using this regularizer. Our findings show that leveraging the sparsity of the derivatives of optical flow reduces computational complexity and memory needs.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3382818" target="_blank">https://dx.doi.org/10.1109/access.2024.3382818</a></p>2024-01-02T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3382818https://figshare.com/articles/journal_contribution/Dense_Optical_Flow_Estimation_Using_Sparse_Regularizers_From_Reduced_Measurements/26490946CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264909462024-01-02T03:00:00Z |
| spellingShingle | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements Muhammad Wasim Nawaz (19325677) Information and computing sciences Computer vision and multimedia computation Energy minimization motion discontinuities optical flow sparse regularizers total variation Optical flow TV Estimation Anisotropic Computer vision Minimization Image motion analysis Energy management Motion control |
| status_str | publishedVersion |
| title | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements |
| title_full | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements |
| title_fullStr | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements |
| title_full_unstemmed | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements |
| title_short | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements |
| title_sort | Dense Optical Flow Estimation Using Sparse Regularizers From Reduced Measurements |
| topic | Information and computing sciences Computer vision and multimedia computation Energy minimization motion discontinuities optical flow sparse regularizers total variation Optical flow TV Estimation Anisotropic Computer vision Minimization Image motion analysis Energy management Motion control |