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Detection of pedestrians in aerial imagery captured by drones has manyapplications including intersection monitoring, patrolling, and surveillance,to name a few. However, the problem is involved due to continuouslychangingcamera viewpoint and object appearance as well as the need for lightweightalgorithms to run on on-board embedded systems. To address this issue, thepaper proposes a framework for pedestrian detection in videos based on the YOLOobject detection network [6] while having a high throughput of more than 5 FPSon the Jetson TX2 embedded board. The framework exploits deep learning forrobust operation and uses a pre-trained model without the need for anyadditional training which makes it flexible to apply on different setups withminimum amount of tuning. The method achieves ~81 mAP when applied on a samplevideo from the Embedded Real-Time Inference (ERTI) Challenge where pedestriansare monitored by a UAV.

Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2