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GTC ON-DEMAND

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Abstract:
Learn how Edge Computing can help you find the parking spot right next to you! We will present a scalable end-to-end architecture that, leveraging on Nvidia Jetson computational power to detect free parking spaces, is able to drive the user minimizing the time spent looking for parking. Using our pre-trained models, we are able to perform the detection at the edge of the cloud, reducing the bandwidth utilization up to 95% with respect to a streaming-based solution. Using Computer Vision and Machine Learning algorithms, the configuration needed to setup the system takes only a few minutes with minimal user interaction. Our optimized with dual boot operating system and support to failover, moreover, guarantees security against malicious intrusions and reliability in the upgrade procedures.
Learn how Edge Computing can help you find the parking spot right next to you! We will present a scalable end-to-end architecture that, leveraging on Nvidia Jetson computational power to detect free parking spaces, is able to drive the user minimizing the time spent looking for parking. Using our pre-trained models, we are able to perform the detection at the edge of the cloud, reducing the bandwidth utilization up to 95% with respect to a streaming-based solution. Using Computer Vision and Machine Learning algorithms, the configuration needed to setup the system takes only a few minutes with minimal user interaction. Our optimized with dual boot operating system and support to failover, moreover, guarantees security against malicious intrusions and reliability in the upgrade procedures.  Back
 
Topics:
Intelligent Video Analytics, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8352
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Abstract:

Park Smart is a solution to lead drivers to find free parking spaces, and help parking owners and managers to improve their business. We exploit the paradigm of Edge Computing, moving the computational load from servers in the Cloud to embedded devices located in place. Such a solution dramatically reduces the bandwidth consumption by ~95%. We perform the fine-tuning of a pre-trained CNN model able to classify empty vs. non empty parking lots using the NVIDIA Jetson inside our AISee box, and then we stream the result to the Cloud as a JSON file. A DL pipeline allows us to have a more robust classification with respect to classical CV techniques. We will present our end-to-end architecture together with the results of the benchmark tests about fine-tuning and classification on TXn.

Park Smart is a solution to lead drivers to find free parking spaces, and help parking owners and managers to improve their business. We exploit the paradigm of Edge Computing, moving the computational load from servers in the Cloud to embedded devices located in place. Such a solution dramatically reduces the bandwidth consumption by ~95%. We perform the fine-tuning of a pre-trained CNN model able to classify empty vs. non empty parking lots using the NVIDIA Jetson inside our AISee box, and then we stream the result to the Cloud as a JSON file. A DL pipeline allows us to have a more robust classification with respect to classical CV techniques. We will present our end-to-end architecture together with the results of the benchmark tests about fine-tuning and classification on TXn.

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Topics:
Computer Vision, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23139
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