비디오 모니터링 환경에서 정확한 돼지 탐지
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 안한세 | - |
dc.contributor.author | 손승욱 | - |
dc.contributor.author | 유승현 | - |
dc.contributor.author | 서유일 | - |
dc.contributor.author | 손준형 | - |
dc.contributor.author | 이세준 | - |
dc.contributor.author | 정용화 | - |
dc.contributor.author | 박대희 | - |
dc.date.accessioned | 2022-03-06T21:40:54Z | - |
dc.date.available | 2022-03-06T21:40:54Z | - |
dc.date.created | 2022-02-10 | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 1229-7771 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/138030 | - |
dc.description.abstract | Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig’s bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국멀티미디어학회 | - |
dc.title | 비디오 모니터링 환경에서 정확한 돼지 탐지 | - |
dc.title.alternative | Accurate Pig Detection for Video Monitoring Environment | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 박대희 | - |
dc.identifier.bibliographicCitation | 멀티미디어학회논문지, v.24, no.7, pp.890 - 902 | - |
dc.relation.isPartOf | 멀티미디어학회논문지 | - |
dc.citation.title | 멀티미디어학회논문지 | - |
dc.citation.volume | 24 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 890 | - |
dc.citation.endPage | 902 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002741569 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Deep Learning | - |
dc.subject.keywordAuthor | Image Processing | - |
dc.subject.keywordAuthor | Pig Detection | - |
dc.subject.keywordAuthor | Real-Time Video Monitoring | - |
dc.subject.keywordAuthor | Video Object Detection | - |
dc.subject.keywordAuthor | YOLO | - |
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