A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ju, Miso | - |
dc.contributor.author | Choi, Younchang | - |
dc.contributor.author | Seo, Jihyun | - |
dc.contributor.author | Sa, Jaewon | - |
dc.contributor.author | Lee, Sungju | - |
dc.contributor.author | Chung, Yongwha | - |
dc.contributor.author | Park, Daihee | - |
dc.date.accessioned | 2021-09-02T10:41:31Z | - |
dc.date.available | 2021-09-02T10:41:31Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2018-06 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/75073 | - |
dc.description.abstract | Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | IMAGE-ANALYSIS | - |
dc.subject | WEIGHT | - |
dc.title | A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Sungju | - |
dc.contributor.affiliatedAuthor | Chung, Yongwha | - |
dc.contributor.affiliatedAuthor | Park, Daihee | - |
dc.identifier.doi | 10.3390/s18061746 | - |
dc.identifier.scopusid | 2-s2.0-85047882136 | - |
dc.identifier.wosid | 000436774300080 | - |
dc.identifier.bibliographicCitation | SENSORS, v.18, no.6 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 18 | - |
dc.citation.number | 6 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | IMAGE-ANALYSIS | - |
dc.subject.keywordPlus | WEIGHT | - |
dc.subject.keywordAuthor | agriculture IT | - |
dc.subject.keywordAuthor | computer vision | - |
dc.subject.keywordAuthor | depth information | - |
dc.subject.keywordAuthor | touching-objects segmentation | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | YOLO | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea 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.