  There is a rapid growth of applications of Unmanned Aerial Vehicles (UAVs) in
traffic management, such as traffic surveillance, monitoring, and incident
detection. However, the existing literature lacks solutions to real-time
incident detection while addressing privacy issues in practice. This study
explored real-time vehicle detection algorithms on both visual and infrared
cameras and conducted experiments comparing their performance. Red Green Blue
(RGB) videos and thermal images were collected from a UAS platform along
highways in the Tampa, Florida, area. Experiments were designed to quantify the
performance of a real-time background subtraction-based method in vehicle
detection from a stationary camera on hovering UAVs under free-flow conditions.
Several parameters were set in the experiments based on the geometry of the
drone and sensor relative to the roadway. The results show that a background
subtraction-based method can achieve good detection performance on RGB images
(F1 scores around 0.9 for most cases), and a more varied performance is seen on
thermal images with different azimuth angles. The results of these experiments
will help inform the development of protocols, standards, and guidance for the
use of drones to detect highway congestion and provide input for the
development of incident detection algorithms.
