Detection of pedestrians in aerial imagery captured by drones has many applications including intersection monitoring, patrolling, and surveillance, to name a few. However, the problem is involved due to continuouslychanging camera viewpoint and object appearance as well as the need for lightweight algorithms to run on on-board embedded systems. To address this issue, the paper proposes a framework for pedestrian detection in videos based on the YOLO object detection network [6] while having a high throughput of more than 5 FPS on the Jetson TX2 embedded board. The framework exploits deep learning for robust operation and uses a pre-trained model without the need for any additional training which makes it flexible to apply on different setups with minimum amount of tuning. The method achieves ~81 mAP when applied on a sample video from the Embedded Real-Time Inference (ERTI) Challenge where pedestrians are monitored by a UAV.
@article{arxiv.1905.06653,
title = {Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2},
author = {Mohamed Afifi and Yara Ali and Karim Amer and Mahmoud Shaker and Mohamed ElHelw},
journal= {arXiv preprint arXiv:1905.06653},
year = {2019}
}