Ahmed S. Kaseb

A system for large-scale analysis of distributed cameras

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).

Cloud Resource Management for Image and Video Analysis of Big Data from Network Cameras

With the existence of millions of public network cameras capturing countless events around the world, there is a need for a system to retrieve, save, and analyze the tremendous amount of visual data from the cameras. The knowledge from the data will ultimately help better understand the world. Such a system needs to allocate and manage significant amounts of resources in order to meet the analysis requirements. In order to reduce the overall analysis cost, this paper presents a cloud resource manager that allocates cost-effective cloud instances, monitors and automatically scales the cloud resources. The paper presents a system that uses the proposed resource manager for image and video analysis of the big data from global network cameras. Our experiments show that the resource manager can lead to 13% reduction in cost. The experiments use four analysis programs which represent different workloads in terms of CPU and memory. The experiments show that different cloud instances are more cost-effective for different analysis programs. One experiment analyzes data streams from 1026 cameras simultaneously for six hours using different analysis programs at different frame rates. The experiment analyzes 5.5 million images, totalling 260GB data.

Harvest the Information from Multimedia Big Data in Global Camera Networks

Many network cameras have been deployed for various purposes, such as monitoring traffic, watching natural scenes, and observing weather. The data from these cameras may provide valuable information about the world. This paper describes the unexplored opportunities and challenges to harvest the information in the global camera networks. A cloud-based system is proposed to harvest the information from many cameras in an efficient way. This system provides an application programming interface (API) that allows users to analyze the multimedia big data from many cameras simultaneously. Users may also store the data for off-line analysis. This system uses the cloud for computing and storage. Experiments demonstrate the ability to process millions of images from thousands of cameras within several hours.

An interactive web-based system using cloud for large-scale visual analytics

Network cameras have been growing rapidly in recent years. Thousands of public network cameras provide tremendous amount of visual information about the environment. There is a need to analyze this valuable information for a better understanding of the world around us. This paper presents an interactive web-based system that enables users to execute image analysis and computer vision techniques on a large scale to analyze the data from more than 65,000 worldwide cameras. This paper focuses on how to use both the systems website and Application Programming Interface (API). Given a computer program that analyzes a single frame, the user needs to make only slight changes to the existing program and choose the cameras to analyze. The system handles the heterogeneity of the geographically distributed cameras, e.g. different brands, resolutions. The system allocates and manages Amazon EC2 and Windows Azure cloud resources to meet the analysis requirements.

Location Based Cloud Resource Management for Analyzing Real-Time Videos from Globally Distributed Network Cameras

The use of video data for a variety of applications has gained immense popularity. These applications include traffic monitoring, surveillance, retail store management, etc. Thousands of publicly accessible network cameras distributed around the world are potential sources of video data. The applications analyzing data from network cameras have different resource requirements (CPU, memory, etc.) and performance requirements (video frame rate). A preferred way to meet these requirements is to use a cloud infrastructure and a pay-per-use model of the cloud. Cloud computing offers resources, referred to as cloud instances, with different capacities and at different locations. The cost of these instances are dependent on their capacities and locations. The frame rate of the video data obtained from a network camera impacts the accuracy of the analysis and is dependent on the location of the cloud instance. Hence it is important to select the locations of the instances to meet the application performance requirements. This paper presents a resource management approach to select the locations, types, and number of cloud instances to analyze real-time video data from the network cameras while meeting the performance requirements and reducing the analysis cost. We model the resource management problem as a variable size bin packing problem and describe a heuristic algorithm to find a solution. This paper uses Amazon EC2 to evaluate our new resource manager, and observes that our method can reduce the analysis cost up to 56% compared with two other strategies for selecting instance locations.

Cloud Resource Management for Analyzing Big Real-Time Visual Data from Network Cameras

Thousands of network cameras stream real-time visual data for different environments, such as streets, shopping malls, and natural scenes. The big visual data from these cameras can be useful for many applications, but analyzing the large quantities of data requires significant amounts of resources. These resources can be obtained from cloud vendors offering cloud instances (referred to as instances in this paper) with different capabilities and hourly costs. It is a challenging problem to manage cloud resources to reduce the cost for analyzing the big real-time visual data from network cameras while meeting the performance requirements. That is because the problem is affected by many factors related to the analysis programs, the cameras, and the instances. This paper proposes a cloud resource manager (referred to as manager in this paper) that aims at solving this problem. The manager estimates the resource requirements of analyzing the data stream from each camera, formulates the resource allocation problem as a 2D vector bin packing problem, and solves it using a heuristic algorithm. The resource manager monitors the allocated instances; it allocates more instances if needed and deallocates existing instances to reduce the cost if possible. The experiments show that the resource manager is able to reduce up to 60 percent of the overall cost. The experiments use multiple analysis programs, such as moving objects detection, feature tracking, and human detection. One experiment analyzes more than 97 million images (3.3 TB of data) from 5,310 cameras simultaneously over 24 hours using 15 Amazon EC2 instances costing

Worldview and route planning using live public cameras

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