DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김정욱 | ko |
dc.contributor.author | 노용만 | ko |
dc.date.accessioned | 2017-09-08T05:22:55Z | - |
dc.date.available | 2017-09-08T05:22:55Z | - |
dc.date.created | 2017-08-29 | - |
dc.date.created | 2017-08-29 | - |
dc.date.created | 2017-08-29 | - |
dc.date.created | 2017-08-29 | - |
dc.date.created | 2017-08-29 | - |
dc.date.created | 2017-08-29 | - |
dc.date.issued | 2017-07 | - |
dc.identifier.citation | 멀티미디어학회논문지, v.20, no.7, pp.986 - 993 | - |
dc.identifier.issn | 1229-7771 | - |
dc.identifier.uri | http://hdl.handle.net/10203/225673 | - |
dc.description.abstract | Object Tracking is a technique for tracking moving objects over time in a video image. Using object tracking technique, many research are conducted such a detecting dangerous situation and recognizing the movement of nearby objects in a smart car. However, it still remains a challenging task such as occlusion, deformation, background clutter, illumination variation, etc. In this paper, we propose a novel deep visual object tracking method that can be operated in robust to many challenging task. For the robust visual object tracking, we proposed a Convolutional Neural Network(CNN) which shares weight of the convolutional layers. Input of the CNN is a three; first frame object image, object image in a previous frame, and current search frame containing the object movement. Also we propose a method to consider the motion of the object when determining the current search area to search for the location of the object. Extensive experimental results on a authorized resource database showed that the proposed method outperformed than the conventional methods. | - |
dc.language | Korean | - |
dc.publisher | 한국멀티미디어학회 | - |
dc.title | 객체의 움직임을 고려한 탐색영역 설정에 따른 가중치를 공유하는 CNN구조 기반의 객체 추적 | - |
dc.title.alternative | Object Tracking based on Weight Sharing CNN Structure according to Search Area Setting Method Considering Object Movement | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.citation.volume | 20 | - |
dc.citation.issue | 7 | - |
dc.citation.beginningpage | 986 | - |
dc.citation.endingpage | 993 | - |
dc.citation.publicationname | 멀티미디어학회논문지 | - |
dc.identifier.doi | 10.9717/kmms.2017.20.7.986 | - |
dc.identifier.kciid | ART002247518 | - |
dc.contributor.localauthor | 노용만 | - |
dc.contributor.nonIdAuthor | 김정욱 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Object Tracking | - |
dc.subject.keywordAuthor | Convolutional Neural Network | - |
dc.subject.keywordAuthor | Search Area | - |
dc.subject.keywordAuthor | Object Movement | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.