The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
비디오 분석은 첫 번째 단계로 비디오 디코딩이 필요할 뿐만 아니라 일반적으로 디코딩된 프레임에 복잡한 컴퓨터 비전 및 기계 학습 알고리즘을 적용하기 때문에 시간이 많이 걸립니다. 프레임 크기가 증가함에 따라 비디오 분석의 효율성을 높이기 위해 Hadoop을 이용한 분산 비디오 처리에 대한 많은 연구가 수행되었습니다. 그러나 대부분의 접근 방식은 여러 노드에서 여러 비디오 파일을 처리하는 데 중점을 두었습니다. 이러한 접근 방식을 사용하려면 속도를 높이려면 여러 개의 비디오 파일이 필요하며, 비디오 파일 자체가 순차적으로 처리되기 때문에 비디오 파일의 크기가 상당히 길면 로드 불균형이 쉽게 발생할 수 있습니다. 대조적으로 우리는 확장된 FFmpeg와 VideoRecordReader를 사용하여 하나의 대용량 비디오 파일을 Hadoop의 여러 노드에서 병렬로 처리할 수 있는 분산 비디오 디코딩 방법을 제안합니다. 실험 결과, 얼굴 검출과 SURF 시스템의 사례 연구는 각 노드에 40.6개의 매퍼가 있는 29.1노드 클러스터에서 각각 12배와 XNUMX배의 속도 향상을 달성하여 좋은 확장성을 보여줍니다.
Illo YOON
University of Seoul
Saehanseul YI
University of Seoul
Chanyoung OH
University of Seoul
Hyeonjin JUNG
University of Seoul
Youngmin YI
University of Seoul
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Illo YOON, Saehanseul YI, Chanyoung OH, Hyeonjin JUNG, Youngmin YI, "Distributed Video Decoding on Hadoop" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 2933-2941, December 2018, doi: 10.1587/transinf.2018PAP0014.
Abstract: Video analytics is usually time-consuming as it not only requires video decoding as a first step but also usually applies complex computer vision and machine learning algorithms to the decoded frame. To achieve high efficiency in video analytics with ever increasing frame size, many researches have been conducted for distributed video processing using Hadoop. However, most approaches focused on processing multiple video files on multiple nodes. Such approaches require a number of video files to achieve any speedup, and could easily result in load imbalance when the size of video files is reasonably long since a video file itself is processed sequentially. In contrast, we propose a distributed video decoding method with an extended FFmpeg and VideoRecordReader, by which a single large video file can be processed in parallel across multiple nodes in Hadoop. The experimental results show that a case study of face detection and SURF system achieve 40.6 times and 29.1 times of speedups respectively on a four-node cluster with 12 mappers in each node, showing good scalability.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018PAP0014/_p
부
@ARTICLE{e101-d_12_2933,
author={Illo YOON, Saehanseul YI, Chanyoung OH, Hyeonjin JUNG, Youngmin YI, },
journal={IEICE TRANSACTIONS on Information},
title={Distributed Video Decoding on Hadoop},
year={2018},
volume={E101-D},
number={12},
pages={2933-2941},
abstract={Video analytics is usually time-consuming as it not only requires video decoding as a first step but also usually applies complex computer vision and machine learning algorithms to the decoded frame. To achieve high efficiency in video analytics with ever increasing frame size, many researches have been conducted for distributed video processing using Hadoop. However, most approaches focused on processing multiple video files on multiple nodes. Such approaches require a number of video files to achieve any speedup, and could easily result in load imbalance when the size of video files is reasonably long since a video file itself is processed sequentially. In contrast, we propose a distributed video decoding method with an extended FFmpeg and VideoRecordReader, by which a single large video file can be processed in parallel across multiple nodes in Hadoop. The experimental results show that a case study of face detection and SURF system achieve 40.6 times and 29.1 times of speedups respectively on a four-node cluster with 12 mappers in each node, showing good scalability.},
keywords={},
doi={10.1587/transinf.2018PAP0014},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - Distributed Video Decoding on Hadoop
T2 - IEICE TRANSACTIONS on Information
SP - 2933
EP - 2941
AU - Illo YOON
AU - Saehanseul YI
AU - Chanyoung OH
AU - Hyeonjin JUNG
AU - Youngmin YI
PY - 2018
DO - 10.1587/transinf.2018PAP0014
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - Video analytics is usually time-consuming as it not only requires video decoding as a first step but also usually applies complex computer vision and machine learning algorithms to the decoded frame. To achieve high efficiency in video analytics with ever increasing frame size, many researches have been conducted for distributed video processing using Hadoop. However, most approaches focused on processing multiple video files on multiple nodes. Such approaches require a number of video files to achieve any speedup, and could easily result in load imbalance when the size of video files is reasonably long since a video file itself is processed sequentially. In contrast, we propose a distributed video decoding method with an extended FFmpeg and VideoRecordReader, by which a single large video file can be processed in parallel across multiple nodes in Hadoop. The experimental results show that a case study of face detection and SURF system achieve 40.6 times and 29.1 times of speedups respectively on a four-node cluster with 12 mappers in each node, showing good scalability.
ER -