Human motion tracking by combining view-based and model-based methods for monocular video sequences
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, J. | - |
dc.contributor.author | Park, S. | - |
dc.contributor.author | Aggarwal, J.K. | - |
dc.date.accessioned | 2022-03-14T09:43:25Z | - |
dc.date.available | 2022-03-14T09:43:25Z | - |
dc.date.created | 2022-03-14 | - |
dc.date.issued | 2003 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/26608 | - |
dc.description.abstract | Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines view-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classify individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on parameter optimization. We convert the human motion tracking problem into a parameter optimization problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging. © Springer-Verlag 2003. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Human motion tracking by combining view-based and model-based methods for monocular video sequences | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, J. | - |
dc.identifier.doi | 10.1007/3-540-44842-x_66 | - |
dc.identifier.scopusid | 2-s2.0-35248892976 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.2669, pp.650 - 659 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 2669 | - |
dc.citation.startPage | 650 | - |
dc.citation.endPage | 659 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
94, Wausan-ro, Mapo-gu, Seoul, 04066, Korea02-320-1314
COPYRIGHT 2020 HONGIK UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.