Multimedia content creation using global network cameras: The making of CAM2

Kaseb, Ahmed S.; Koh, Youngsol; Berry, Everett; McNulty, Kyle; Lu, Yung-Hsiang; Delp, Edward J.

doi:10.1109/globalsip.2015.7416927

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…That is, a lot of images are stored that are not seen by anyone. Therefore, there is a demand for techniques to re-create and present images from these huge image datasets in desired forms [24][25][26]. As an example of such forms, the authors have been conducting research on image generation of DR [4], which is one of the missing components of augmented visualization.…”

Section: I F R E E -V I E W P O I N T I M a G E / V I D E Omentioning

confidence: 99%

An overview of augmented visualization: observing the real world as desired

Mori

Saitō

2018

SIP

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Over 20 years have passed since a free-viewpoint video technology has been proposed with which a user's viewpoint can be freely set up in a reconstructed three-dimensional space of a target scene photographed by multi-view cameras. This technology allows us to capture and reproduce the real world as recorded. Once we capture the world in a digital form, we can modify it as augmented reality (i.e., placing virtual objects in the digitized real world). Unlike this concept, the augmented world allows us to see through real objects by synthesizing the backgrounds that cannot be observed in our raw perspective directly. The key idea is to generate the background image using multi-view cameras, observing the backgrounds at different positions and seamlessly overlaying the recovered image in our digitized perspective. In this paper, we review such desired view-generation techniques from the perspective of free-view point image generation and discuss challenges and open problems through a case study of our implementations.

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Section: I F R E E -V I E W P O I N T I M a G E / V I D E Omentioning

confidence: 99%

An overview of augmented visualization: observing the real world as desired

Mori

Saitō

2018

SIP

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“…To drive the research in cloud resource optimization for processing multiple visual data streams, a software infrastructure named Continuous Analysis of Many Cameras (CAM 2 ) has been established at Purdue University [3]. CAM 2 uses network cameras that provide realtime visual data publicly available on the Internet.…”

Section: Introductionmentioning

confidence: 99%

Cloud Resource Optimization for Processing Multiple Streams of Visual Data

et al. 2019

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Hundreds of millions of network cameras have been installed throughout the world. Each is capable of providing a vast amount of real-time data. Analyzing the massive data generated by these cameras requires significant computational resources and the demands may vary over time. Cloud computing shows the most promise to provide the needed resources on demand. In this article, we investigate how to allocate cloud resources when analyzing real-time data streams from network cameras. A resource manager considers many factors that affect its decisions, including the types of analysis, the number of data streams, and the locations of the cameras. The manager then selects the most cost-efficient types of cloud instances (e.g. CPU vs. GPGPU) to meet the computational demands for analyzing streams. We evaluate the effectiveness of our approach using Amazon Web Services. Experiments demonstrate more than 50% cost reduction for real workloads.

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“…Finding network cameras and the metadata about these cameras is part of the Purdue CAM2 (Continuous Analysis of Many CAMeras) project [7]. CAM2 is a computing platform for analyzing real-time data from network cameras.…”

Section: Introductionmentioning

confidence: 99%

Creating the World's Largest Real-Time Camera Network

Dailey¹,

Kaseb²,

Brown³

et al. 2017

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Millions of cameras are openly connected to the Internet for a variety of purposes. This paper takes advantage of this resource to gather visual data. This camera data could be used for a myriad of purposes by solving two problems. (i) The network camera image data needs context to solve real world problems. (ii) While contextual data is available, it is not centrally aggregated. The goal is to make it easy to leverage the vast amount of network cameras. The database allows users to aggregate camera data from over 119,000 network camera sources all across the globe in real time. This paper explains how to collect publicly available information from network cameras. The paper describes how to analyze websites to retrieve relevant information about the cameras and to calculate the refresh rates of the cameras.

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Multimedia content creation using global network cameras: The making of CAM2

Cited by 6 publications

References 14 publications

An overview of augmented visualization: observing the real world as desired

An overview of augmented visualization: observing the real world as desired

Cloud Resource Optimization for Processing Multiple Streams of Visual Data

Creating the World's Largest Real-Time Camera Network

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