PICASO: PIxel correspondences and SOft match selection for real-time tracking
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Timofte, Radu | - |
dc.contributor.author | Kwon, Junseok | - |
dc.contributor.author | Van Gool, Luc | - |
dc.date.available | 2020-06-16T05:20:37Z | - |
dc.date.issued | 2016-12 | - |
dc.identifier.issn | 1077-3142 | - |
dc.identifier.issn | 1090-235X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40678 | - |
dc.description.abstract | Visual tracking is one of the computer vision's longstanding challenges, with many methods as a result. While most state-of-the-art methods trade-off performance for speed, we propose PICASO, an efficient, yet strongly performing tracking scheme. The target object is modeled as a set of pixel-level templates with weak configuration constraints. The pixels of a search window are matched against those of the surrounding context and of the object model. To increase the robustness, we match also from the object to the search window, and the pairs matching in both directions are the correspondences used to localize. This localization process is robust, also against occlusions which are explicitly modeled. Another source of robustness is that the model - as in several other modern trackers -gets constantly updated over time with newly incoming information about the target appearance. Each pixel is described by its local neighborhood. The match of a pixel is taken to be the one with the largest contribution in its sparse decomposition over a set of pixels. For this soft match selection, we analyze both l(1) and l(2)-regularized least squares formulations and the recently proposed l(1)-constrained 'Iterative Nearest Neighbors' approach. We evaluate our tracker on standard videos for rigid and non-rigid object tracking. We obtain excellent performance at 42fps with Matlab on a CPU. (C) 2016 Elsevier Inc. All rights reserved. | - |
dc.format.extent | 12 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.title | PICASO: PIxel correspondences and SOft match selection for real-time tracking | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.cviu.2016.02.002 | - |
dc.identifier.bibliographicCitation | COMPUTER VISION AND IMAGE UNDERSTANDING, v.153, pp 151 - 162 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000389566500015 | - |
dc.identifier.scopusid | 2-s2.0-84995903063 | - |
dc.citation.endPage | 162 | - |
dc.citation.startPage | 151 | - |
dc.citation.title | COMPUTER VISION AND IMAGE UNDERSTANDING | - |
dc.citation.volume | 153 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Visual tracking | - |
dc.subject.keywordAuthor | Non-rigid tracking | - |
dc.subject.keywordAuthor | Linear decomposition | - |
dc.subject.keywordAuthor | Pixel correspondences | - |
dc.subject.keywordAuthor | Iterative nearest neighbors | - |
dc.subject.keywordPlus | OBJECT TRACKING | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang 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.