TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Scalable Big Data Pipeline for Video Stream Analytics Over Commodity Hardware |
| 영문제목(English Title) |
Scalable Big Data Pipeline for Video Stream Analytics Over Commodity Hardware |
| 저자(Author) |
Umer Ayub
Syed M. Ahsan
Shavez M. Qureshi
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| 원문수록처(Citation) |
VOL 16 NO. 03 PP. 1146 ~ 1165 (2022. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.
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| 키워드(Keyword) |
Video Analytics
Big Data
Data Pipeline
Spark
Kafka
OpenCV
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| 파일첨부 |
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