Video stream analysis in clouds: An object detection and classification framework for high performance video analytics

Tariq, M.; Anjum, Ashiq; Abdullah, Tariq; Tariq, M. Fahim; Baltaci, Yusuf; Antonopoulos, Nick

Abstract

Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involvement of human factor. We present a cloud based video analytics framework for scalable and robust analysis of video streams. The framework empowers an operator by automating the object detection and classification process from recorded video streams. An operator only specifies an analysis criteria and duration of video streams to analyse. The streams are then fetched from a cloud storage, decoded and analysed on the cloud. The framework executes compute intensive parts of the analysis to GPU powered servers in the cloud. Vehicle and face detection are presented as two case studies for evaluating the framework, with one month of data and a 15 node cloud. The framework reliably 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 deployment of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least twice as fast than the cloud deployment without GPUs.

• Journal Article Type: Article
• Acceptance Date: Jan 13, 2016
• Online Publication Date: Jan 13, 2016
• Publication Date: Jan 13, 2016
• Deposit Date: Feb 12, 2019
• Journal: IEEE Transactions on Cloud Computing
• Print ISSN: 2168-7161
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/TCC.2016.2517653
• Keywords: Cloud Computing , Video Stream Analytics , Object Detection , Object Classification , High Performance
• Public URL: http://researchrepository.napier.ac.uk/Output/1557058