For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Distributed Video Decoding on Hadoop
Illo YOON Saehanseul YI Chanyoung OH Hyeonjin JUNG Youngmin YI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/12/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
Category: Cluster Computing
distributed video processing, Hadoop, extended FFmpeg,
Full Text: PDF(1.1MB)
>>Buy this Article
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.