A Hog & Graph Based Human Segmentation from Video Sequences

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dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authoridDönmez, Emrah/0000-0003-3345-8344
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.authorwosidDönmez, Emrah/W-2891-2017
dc.contributor.authorDonmez, Emrah
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:45:45Z
dc.date.available2024-08-04T20:45:45Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractHuman segmentation from video is a significant problem to recognize a specific person which is desired to find. There are a remarkable number of studies discussing segmentation process in videos. Almost all study is examined how well a human segmentation process could be created by considering their position, clothing and face. Unfortunately, it has been assumed that the background is static by approximately all of these works. Main challenges in this research area are; non-static background derived from dynamic structure of videos, human body and face position, shadows and clothing changing after a period of time in videos. In this script, we have proposed a multi-model (Histogram of the Gradients - HOG and Graph-based) human segmentation technique that has worked with respect to HOG features and detected low-level and mid-level features of human clothing by supposing their positions in video. The offered technique is designed to demonstrate robustness against such challenges emphasized above. In this study, a well-known video series have been used. The video scenes can reach 15 similar to 25 fps and have the size about 640x480px. To compare graph-based method robustness a well-known segmentation method Watershed is also experimented and both methods are simply compared. Eventually, we determined that the proposed technique can produce satisfactory quality segmentation mentioned and detailed in following sections.en_US
dc.description.sponsorshipInonu Univ, Comp Sci Dept,IEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-6878-8
dc.identifier.scopus2-s2.0-85062497157en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98680
dc.identifier.wosWOS:000458717400065en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHuman segmentationen_US
dc.subjectHOGen_US
dc.subjectWatersheden_US
dc.subjectGraph-based segmentationen_US
dc.titleA Hog & Graph Based Human Segmentation from Video Sequencesen_US
dc.typeConference Objecten_US

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