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VIDEO STREAM ANALYSIS IN CLOUDS: AN OBJECT DETECTION AND
CLASSIFICATION FRAMEWORK FOR HIGH PERFORMANCE VIDEO ANALYTICS
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 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.