# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
###### Abstract  
The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage. U  
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(quadcopters, hexacopters, and octocopters), and fixed-wing hybrid VTOL UAV systems [6]. Fixed wing drones offer great utility for package deliveries and remote inspections, while multirotor drones are greatly considered for search and rescue operations due to their hovering capabilities [7]. For recreational purposes, such as photography and high-resolution aerial imaging, smaller drones equipped with professional-grade cameras are used [8]. The applications, pros, and cons of each drone type are discussed in Table 1.  
Among the popular civil applications of drones, road traffic monitoring (RTM) systems have witnessed significant development. An RTM system primarily focuses on two tasks: detecting road accidents and identifying traffic congestion [9]. However, traditional surveillance methods lack the aerial perspective of UAVs, limiting a comprehensive analysis [10]. With the integration of global navigation satellite systems (GNSS), UAVs offer researchers a geospatial viewpoint, enabling them to conduct meaningful research in the field [11]. In recent times, there have been significant advancements in vehicle detection methods through the use of computer vision and deep learning techniques [12]. These technologies have greatly enhanced the capabilities of object detection and tracking methods, which are vital for tasks such as estimating vehicle trajectories and analyzing traffic flow [13]. Without accurate speed measurement, it is unviable to implement an accurate RTM system. Despite these advancements in computer vision, there are still technical limitations that need to be addressed [14]. Much of the existing literature focuses on RTM systems that rely on fixed camera systems with limited spatial coverage. In contrast, a moving drone can provide increased mobility, better spatial coverage, and reduced blind spots. Additionally, the current speed estimation techniques used by law enforcement only capture a single point speed (using LiDAR-based systems), which may not be sufficient for comprehensive analysis and could hinder the decisionmaker's ability to implement appropriate traffic control measures.