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GTC ON-DEMAND

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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
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