Robust Real-time Pedestrian Detection in Aerial Imagery on Jetson TX2
This addresses the problem of real-time pedestrian detection for drone-based applications like surveillance, but it is incremental as it adapts an existing method to a specific hardware setup.
The paper tackled pedestrian detection in aerial drone imagery by proposing a YOLO-based framework that achieves ~81 mAP on a sample video from the ERTI Challenge and runs at over 5 FPS on the Jetson TX2 embedded board.
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.