Home > Computer Science > Multimedia > Volume-3 > Issue-1 > Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud

Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud

Call for Papers

Volume-3 | Issue-1

Last date : 30 Dec 2018

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

Processing Charges : 700/- INR Only OR 25 USD (for foreign users)

Paper Publish : Within 2-4 Days after submitting

Submit Paper Online

For Author

IJTSRD Publication

Research Area

News & Events


Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud

R. Gnana bharathy

R. Gnana bharathy "Framework of Object Detection and Classification High Performance Using Video Analytics in Cloud" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18906.pdf

Video analytics framework detection performance is worked at cloud. Object detection and classification are the basic tasks in video analytics and become the initial point for other complex submissions. Old-fashioned video analytics approaches are manual and time consuming. These are particular due to the very participation of human factor. This paper present a cloud based video analytics framework for accessible and robust analysis of video streams. The framework enables an operative by programing the object detection and classification process from recorded video streams. An operative only specifies an analysis criteria and period of video streams to analyze. The streams are then realized from cloud storage, cracked and analyzed on the cloud. The framework performs compute severe parts of the analysis to CPU powered servers in the cloud. Vehicle and face finding are accessible as two case studies for assessing the framework, with one month of data and a 15 node cloud. The framework consistently performed object detection and classification on the data, comprising of 21,600 video streams and 175 GB in size, in 6.52 hours. The GPU enabled placement of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least double as fast than the cloud deployment Without GPUs. The analysis framework is high.

Cloud Computing, Video Stream Analytics, Object Detection, Object Classification, High Performance, and GPU


Volume-3 | Issue-1 , December 2018

2456-6470

IJTSRD18906