Extensible video processing framework in Apache Hadoop

Abstract

Digital video is prominent big data spread all over the Internet. It is large not only in size but also in required processing power to extract useful information. Fast processing of excessive video reels is essential on criminal investigations, such as terrorism. This demo presents an extensible video processing framework in Apache Hadoop to parallelize video processing tasks in a cloud environment. Except for video transcending systems, there have been few systems that can perform various video processing in cloud computing environments. The framework employs FFmpeg for a video coder, and OpenCV for a image processing engine. To optimize the performance, it exploits MapReduce implementation details to minimize video image copy. Moreover, FFmpeg source code was modified and extended, to access and exchange essential data and information with Hadoop, effectively. A face tracking system was implemented on top of the framework for the demo, which traces the continuous face movements in a sequence of video frames. Since the system provides a web-based interface, people can try the system on site. In an 8-core environment with two quad-core systems, the system shows 75% of scalability. © 2013 IEEE.