Title :
HVPI: Extending Hadoop to Support Video Analytic Applications
Author :
Xiaomeng Zhao ; Huadong Ma ; Haitao Zhang ; Yi Tang ; Yue Kou
Author_Institution :
Beijing Key Lab. of Intell. Telecommun. Software & Multimedia, Beijing Univ. of Posts & Telecommun., Beijing, China
Abstract :
Hadoop is widely deployed distributed computing framework and makes creating distributed applications much easier. However, unlike text data, there is no existing video r/w interface for Hadoop, and many existing video analytic applications implemented in C/C++ are not compatible with Hadoop framework. In this paper, we propose an open source Hadoop video processing interface HVPI to extend Hadoop to support video analytic applications. It provides easy-to-use video r/w interface for developers to quickly build large-scale video analytic applications based on Hadoop, and native processing interface to help users easily port existing video analytic applications written in C/C++ into Hadoop platform. We also present two typical use cases of HVPI and do experiments based on them. Experimental results demonstrate that the applications built based on HVPI are both scalable and efficient.
Keywords :
parallel processing; video signal processing; C/C++ language; HVPI; Hadoop framework; distributed computing framework; open source Hadoop video processing interface; video analytic applications; video read-write interface; Cameras; Java; Libraries; Ports (Computers); Streaming media; Surveillance; Writing; JNI; distributed video processing; hadoop; hadoop pipes; hadoop steaming; video analysis;
Conference_Titel :
Cloud Computing (CLOUD), 2015 IEEE 8th International Conference on
Conference_Location :
New York City, NY
Print_ISBN :
978-1-4673-7286-2
DOI :
10.1109/CLOUD.2015.109