Video retrieval of human interactions using model-based motion tracking and multi-layer finite state automata
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, S | - |
dc.contributor.author | Park, J | - |
dc.contributor.author | Aggarwal, JK | - |
dc.date.accessioned | 2022-03-14T09:43:47Z | - |
dc.date.available | 2022-03-14T09:43:47Z | - |
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/26627 | - |
dc.description.abstract | Recognition of human interactions in a video is useful for video annotation, automated surveillance, and content-based video retrieval. This paper presents a model-based approach to motion tracking and recognition of human interactions using multi-layer finite state automata (FA). The system is used for widely-available, static-background monocular surveillance videos. A three-dimensional human body model is built using a sphere and cylinders and is projected on a two-dimensional image plane to fit the foreground image silhouette. We convert the human motion tracking problem into a parameter optimization problem without the need to compute inverse kinematics. A cost functional is used to estimate the degree of the overlap between the foreground input image silhouette and a projected three-dimensional body model silhouette. Motion data obtained from the tracker is analyzed in terms of feet, torso, and hands by a behavior recognition system. The recognition model represents human behavior as a sequence of states that register the configuration of individual body parts in space and time. In order to overcome the exponential growth of the number of states that usually occurs in single-level FA, we propose a multi-layer FA that abstracts states and events from motion data at multiple levels: low-level FA analyzes body parts only, and high-level FA analyzes the human interaction. Motion tracking results from video sequences are presented. Our recognition framework successfully recognizes various human interactions such as approaching, departing, pushing, pointing, and handshaking. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER-VERLAG BERLIN | - |
dc.subject | RECOGNITION | - |
dc.title | Video retrieval of human interactions using model-based motion tracking and multi-layer finite state automata | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, J | - |
dc.identifier.wosid | 000185604400039 | - |
dc.identifier.bibliographicCitation | IMAGE AND VIDEO RETRIEVAL, PROCEEDINGS, v.2728, pp.394 - 403 | - |
dc.relation.isPartOf | IMAGE AND VIDEO RETRIEVAL, PROCEEDINGS | - |
dc.citation.title | IMAGE AND VIDEO RETRIEVAL, PROCEEDINGS | - |
dc.citation.volume | 2728 | - |
dc.citation.startPage | 394 | - |
dc.citation.endPage | 403 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.subject.keywordPlus | RECOGNITION | - |
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.