Gait recognition for person re-identification
<p>Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has...
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2022
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| _version_ | 1864513567510757376 |
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| author | Omar Elharrouss (14150895) |
| author2 | Noor Almaadeed (14150898) Somaya Al-Maadeed (5178131) Ahmed Bouridane (2270131) |
| author2_role | author author author |
| author_facet | Omar Elharrouss (14150895) Noor Almaadeed (14150898) Somaya Al-Maadeed (5178131) Ahmed Bouridane (2270131) |
| author_role | author |
| dc.creator.none.fl_str_mv | Omar Elharrouss (14150895) Noor Almaadeed (14150898) Somaya Al-Maadeed (5178131) Ahmed Bouridane (2270131) |
| dc.date.none.fl_str_mv | 2022-11-22T21:14:18Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11227-020-03409-5 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Gait_recognition_for_person_re-identification/21597495 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Information systems Gait recognition Angle estimation Motion detection Convolutional neural networks |
| dc.title.none.fl_str_mv | Gait recognition for person re-identification |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.</p><h2>Other Information</h2> <p> Published in: The Journal of Supercomputing<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="http://dx.doi.org/10.1007/s11227-020-03409-5" target="_blank">http://dx.doi.org/10.1007/s11227-020-03409-5</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_cfd20ea9b11d4f1e80314d28d5508a93 |
| identifier_str_mv | 10.1007/s11227-020-03409-5 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597495 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Gait recognition for person re-identificationOmar Elharrouss (14150895)Noor Almaadeed (14150898)Somaya Al-Maadeed (5178131)Ahmed Bouridane (2270131)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesInformation systemsGait recognitionAngle estimationMotion detectionConvolutional neural networks<p>Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.</p><h2>Other Information</h2> <p> Published in: The Journal of Supercomputing<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="http://dx.doi.org/10.1007/s11227-020-03409-5" target="_blank">http://dx.doi.org/10.1007/s11227-020-03409-5</a></p>2022-11-22T21:14:18ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11227-020-03409-5https://figshare.com/articles/journal_contribution/Gait_recognition_for_person_re-identification/21597495CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215974952022-11-22T21:14:18Z |
| spellingShingle | Gait recognition for person re-identification Omar Elharrouss (14150895) Engineering Electronics, sensors and digital hardware Information and computing sciences Information systems Gait recognition Angle estimation Motion detection Convolutional neural networks |
| status_str | publishedVersion |
| title | Gait recognition for person re-identification |
| title_full | Gait recognition for person re-identification |
| title_fullStr | Gait recognition for person re-identification |
| title_full_unstemmed | Gait recognition for person re-identification |
| title_short | Gait recognition for person re-identification |
| title_sort | Gait recognition for person re-identification |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Information systems Gait recognition Angle estimation Motion detection Convolutional neural networks |