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Thousands of cameras are connected to the Internet providing streaming data (videos or periodic images). The images contain information that can be used to determine the scene contents such as traffic, weather, and the environment. Analyzing the data from these cameras presents many challenges, such as (i) retrieving data from geographically distributed and heterogeneous cameras, (ii) providing a software environment for users to simultaneously analyze large amounts of data from the cameras, (iii) allocating and managing computation and storage resources. This paper presents a system designed to address these challenges. The system enables users to execute image analysis and computer vision techniques on a large scale with only slight changes to the existing methods. It currently includes more than 65,000 cameras deployed worldwide. Users can select cameras for the types of analysis they can do. The system allocates Amazon EC2 and Windows Azure cloud instances for executing the analysis. Our experiments demonstrate that this system can be used for a variety of image analysis techniques (e.g. motion analysis and human detection) using 2.7 million images from 1274 cameras for three hours using 15 cloud instances to analyze 141 GB of images (at 107 Mbps).

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