Distributed Feature Extraction Using Cloud Computing Resources
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The need to expand the computational resources in a massive surveillance network is clear but traditional means of purchasing new equipment for short-term tasks every year is wasteful. In this work I will provide evidence in support of utilizing a cloud computing infrastructure to perform computationally intensive feature extraction tasks on data streams. Efficient off-loading of computational tasks to cloud resources will require a minimization of the time needed to expand the cloud resources, an efficient model of communication and a study of the interplay between the in-network computational resources and remote resources in the cloud. This report provides strong evidence that the use of cloud computing resources in a near real-time distributed sensor network surveillance system, ASAP, is feasible. A face detection web service operating on an Amazon EC2 instance is shown to provide processing of 10-15 frames per second.