Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김기범 | - |
dc.date.accessioned | 2022-08-17T01:33:08Z | - |
dc.date.available | 2022-08-17T01:33:08Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.citation | IEEE ACCESS, v. 9, Page. 111249-111266 | en_US |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://doaj.org/article/beeae16cd98842b991d1f2f699bc8b9d | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/172489 | - |
dc.description.abstract | Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher's Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc. | en_US |
dc.description.sponsorship | This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) under Grant 2018R1D1A1A02085645, in part by Korea Medical Device Development Fund Grant through Korean Government (the Ministry of Science and ICT; the Ministry of Trade, Industry and Energy; the Ministry of Health and Welfare; and the Ministry of Food and Drug Safety) under Grant 202012D05-02, and in part by Hanyang University under Grant 201800000000647. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
dc.subject | 3D point cloud | en_US |
dc.subject | fiducial points | en_US |
dc.subject | human-object interaction | en_US |
dc.subject | pixel labeling | en_US |
dc.subject | semantic segmentation | en_US |
dc.subject | super-pixels | en_US |
dc.subject | Electrical engineering. Electronics. Nuclear engineering | en_US |
dc.subject | TK1-9971 | en_US |
dc.title | Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling | en_US |
dc.type | Article | en_US |
dc.relation.volume | 9 | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3101716 | - |
dc.relation.page | 111249-111266 | - |
dc.relation.journal | IEEE ACCESS | - |
dc.contributor.googleauthor | Khalid, Nida | - |
dc.contributor.googleauthor | Ghadi, Yazeed Yasin | - |
dc.contributor.googleauthor | Gochoo, Munkhjargal | - |
dc.contributor.googleauthor | Jalal, Ahmad | - |
dc.contributor.googleauthor | Kim, Kibum | - |
dc.relation.code | 2021000011 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF COMPUTING[E] | - |
dc.sector.department | SCHOOL OF MEDIA, CULTURE, AND DESIGN TECHNOLOGY | - |
dc.identifier.pid | kibum | - |
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