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GTC ON-DEMAND

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Abstract:
We'll discuss how we're using several Jetson TX2-based edge computing devices and LPWAN networks to monitor in real time the flow of vehicles and pedestrians in a network. In particular, we'll describe our work to better understand and predict pedestrian and vehicle flow around the Liverpool CBD to ease congestion, provide better transport options, and improve health and safety. In our solution, each device in the monitored network processes the live feed from its own camera. We'll explain how we use the YOLO v3 object detector to analyze these frames and extract the pedestrians and vehicles in each, then pass this information onto a tracker algorithm (Kalman filter) to determine their trajectories. After a frame is processed, it is discarded and only aggregated indicators are sent over the LPWAN network to a dashboard to reduce privacy concerns and bandwidth requirements.
We'll discuss how we're using several Jetson TX2-based edge computing devices and LPWAN networks to monitor in real time the flow of vehicles and pedestrians in a network. In particular, we'll describe our work to better understand and predict pedestrian and vehicle flow around the Liverpool CBD to ease congestion, provide better transport options, and improve health and safety. In our solution, each device in the monitored network processes the live feed from its own camera. We'll explain how we use the YOLO v3 object detector to analyze these frames and extract the pedestrians and vehicles in each, then pass this information onto a tracker algorithm (Kalman filter) to determine their trajectories. After a frame is processed, it is discarded and only aggregated indicators are sent over the LPWAN network to a dashboard to reduce privacy concerns and bandwidth requirements.  Back
 
Topics:
Intelligent Video Analytics, Intelligent Machines, IoT & Robotics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9206
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Abstract:

This talk presents how several Jetson TX2-based edge computing devices and LPWAN networks can be used to monitor in real time the flow of vehicles and pedestrian in a network. Each device in the monitored network processes the live feed from its own camera: each frame is analysed by an object detector (Yolo v3) to extract the pedestrians and vehicles in the frame; then the detections passed to a tracker algorithm (Kalman filter) to determine their trajectories. Once a frame has been processed, it is discarded and only aggregated indicators are sent over the LPWAN network to a dashboard (number of detections, types, trajectories), limiting the privacy issues and the bandwidth requirements. This solution is a key component of a project aiming to better understand and predict the pedestrian and vehicles flows around the Liverpool CBD in order to ease congestion, provide better transport options and improve health and safety.

This talk presents how several Jetson TX2-based edge computing devices and LPWAN networks can be used to monitor in real time the flow of vehicles and pedestrian in a network. Each device in the monitored network processes the live feed from its own camera: each frame is analysed by an object detector (Yolo v3) to extract the pedestrians and vehicles in the frame; then the detections passed to a tracker algorithm (Kalman filter) to determine their trajectories. Once a frame has been processed, it is discarded and only aggregated indicators are sent over the LPWAN network to a dashboard (number of detections, types, trajectories), limiting the privacy issues and the bandwidth requirements. This solution is a key component of a project aiming to better understand and predict the pedestrian and vehicles flows around the Liverpool CBD in order to ease congestion, provide better transport options and improve health and safety.

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Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
AI Conference Australia
Year:
2018
Session ID:
AUS8007
Streaming:
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