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GTC ON-DEMAND

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Abstract:
The rapid deployment of video sensors across multiple platforms, such as security cameras, unmanned aerial vehicles, and satellites, has resulted in information overload, outpacing analyst ability to effectively use the capability. State-of-the-art processing, exploitation, and dissemination systems primarily focus on forensic use of stored video for identification and tracking of objects and subjects of interest. Not many capabilities exist to deploy and monitor in real-time 100s and 1,000s sensors in cities, bases, airports, and similar venues. Using cloud deep learning services or APIs (for example, Amazon Rekognition) is not only cost prohibitive, but also presents challenges to organizations with sensitive or classified data. We'll discuss various efforts at the Johns Hopkins University Applied Physics Laboratory to develop inexpensive, low size, weight, and power (SWaP) real-time automatic target recognition systems.
The rapid deployment of video sensors across multiple platforms, such as security cameras, unmanned aerial vehicles, and satellites, has resulted in information overload, outpacing analyst ability to effectively use the capability. State-of-the-art processing, exploitation, and dissemination systems primarily focus on forensic use of stored video for identification and tracking of objects and subjects of interest. Not many capabilities exist to deploy and monitor in real-time 100s and 1,000s sensors in cities, bases, airports, and similar venues. Using cloud deep learning services or APIs (for example, Amazon Rekognition) is not only cost prohibitive, but also presents challenges to organizations with sensitive or classified data. We'll discuss various efforts at the Johns Hopkins University Applied Physics Laboratory to develop inexpensive, low size, weight, and power (SWaP) real-time automatic target recognition systems.  Back
 
Topics:
Computer Vision, Intelligent Video Analytics
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7233
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Abstract:

We will discuss a new Deep Learning architecture developed at the Johns Hopkins University Applied Physics Laboratory (JHU/APL). APL's architecture is based on Docker which allows users to easily train and deploy DL applications in diverse environments, where computation is constrained by power, memory, internet connectivity, and system security requirements.

We will discuss a new Deep Learning architecture developed at the Johns Hopkins University Applied Physics Laboratory (JHU/APL). APL's architecture is based on Docker which allows users to easily train and deploy DL applications in diverse environments, where computation is constrained by power, memory, internet connectivity, and system security requirements.

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Topics:
HPC and AI, Federal
Type:
Talk
Event:
GTC Taiwan
Year:
2016
Session ID:
DCS16160
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