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Real Time Video Copy Detection using Hadoop

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Abrum Jeysudha, Lavanya Muthukutty, Ashwati Krishnan, Samit Shivadekar

Abrum Jeysudha, Lavanya Muthukutty, Ashwati Krishnan and Samit Shivadekar. Real Time Video Copy Detection using Hadoop. International Journal of Computer Applications 162(9):42-45, March 2017. BibTeX

	author = {Abrum Jeysudha and Lavanya Muthukutty and Ashwati Krishnan and Samit Shivadekar},
	title = {Real Time Video Copy Detection using Hadoop},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {162},
	number = {9},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {42-45},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017913376},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Due to emerging interest in videos, there are various sites which provides with different kinds of videos but it is not necessary that every video hold original content. Video Copy Detection process comes into picture to differentiate between original and duplicate videos. Video Copy Detection basically deals with finding out similarities between the content of two given videos. Hadoop is a distributed platform which makes use of MapReduce programming model. It has two phases i.e. Mapping and Reducing phase. Brightness Sequence algorithm along with TIRI-DCT algorithm is implemented to overcome the problems in the existing system. OCR is used in order to detect the copied videos based on subtitles or any other form of text present in the video. The framegrabber(), which is a JAVA method, is used to convert the videos into multiple frames at different time instincts.


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Video copy, TIRI-DCT, Brightness sequence, OCR, training video, querying video, Hadoop, MapReduce, hash, plagiarism, HDFS, FFMPEG, frames, copied video.