Human posture prediction by deep learning
| dc.authorid | 0000-0001-5806-7126 | en_US |
| dc.authorid | 0000-0002-9913-5946 | en_US |
| dc.contributor.author | Kanpak, Hediye Nupelda | |
| dc.contributor.author | Arserim, M. Ali | |
| dc.date.accessioned | 2022-01-28T12:52:36Z | |
| dc.date.available | 2022-01-28T12:52:36Z | |
| dc.date.issued | 2021 | en_US |
| dc.department | Dicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü | en_US |
| dc.description.abstract | İnterpreting the human posture in human videos and pictures constitutes the most basic structure of human posture prediction. A system is created that decides what the movement is and what purpose it is made by evaluating pictures and videos. In this way, a structure has been created that determines and classifies human movements as an automatic system. A mechanism of motional meaning contained in the created system has been recognized in such away that the pattern is expressed. It is intended to take advantage of these components by taking instant information. A result was obtained by primarily inferring instant still images and eliminating time intervals that do not contain information range. A classification was made according to their accuracy. Based on the location coordinates of the images and videos, it was tried to determine how people might react in the neck stage. Thanks to the analysis performed through the joints with optical flow calculation, motion information was obtained and classifications and analyses expressing the power of motion were created. Motion information on the region determined in the image is determined by the detection of joints, revealing the power generated by movement. The created histograms provide ease of classification of motion. Based on the reliability of the descriptions, which include the concept of the time in a sequential way with the detection of joints, it was desired to create a sliding classification mechanism within the framework of these joints. As a result of this study, it was aimed to obtain a functional structure that can recognize and understand the autonomous movement of stationary or moving beings. An efficient structure has been created in terms of providing a useful and facilitating mechanism by solving the problems in estimation. | en_US |
| dc.identifier.citation | Kanpak, H. N. ve Arserim, M. A. (2021). Human posture prediction by deep learning. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(5), 775-782. | en_US |
| dc.identifier.doi | 10.24012/dumf.1051429 | |
| dc.identifier.endpage | 782 | en_US |
| dc.identifier.issn | 1309-8640 | |
| dc.identifier.issn | 2146-4391 | |
| dc.identifier.issue | 5 | en_US |
| dc.identifier.startpage | 775 | en_US |
| dc.identifier.trdizinid | 498864 | |
| dc.identifier.uri | https://dergipark.org.tr/tr/download/article-file/2167844 | |
| dc.identifier.uri | https://hdl.handle.net/11468/9111 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/yayin/detay/498864 | |
| dc.identifier.volume | 12 | en_US |
| dc.indekslendigikaynak | TR-Dizin | |
| dc.institutionauthor | Kanpak, Hediye Nupelda | |
| dc.institutionauthor | Arserim, M. Ali | |
| dc.language.iso | en | en_US |
| dc.publisher | Dicle Üniversitesi Mühendislik Fakültesi | en_US |
| dc.relation.ispartof | Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Human pose estimation | en_US |
| dc.subject | Motion detection | en_US |
| dc.subject | Human body recognition and tracking | en_US |
| dc.title | Human posture prediction by deep learning | en_US |
| dc.title | Human posture prediction by deep learning | |
| dc.type | Article | en_US |
