# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
## 2 Related Works  
Recently, a multitude of research literature has been published tackling a similar problem but focusing on fixed cameras. Computer vision has wider applicability within road traffic monitoring systems and road safety. The biggest challenge of using these solutions is the real-world practical implementation [15]. In their study, the authors of [16] present a real-time vehicle velocity estimation method using a vehicle-mounted monocular camera. The authors' approach involves a computer-vision-based object tracker combined with a small neural network to estimate the velocity of the vehicles from the tracked bounding boxes. To calculate the distance traveled by the vehicles, the authors use the focal length of the monocular camera, a fixed height, and the bottom edge midpoint of the bounding box. The method yields promising results with a vehicle estimation error of 1.28 km/h, but the major limitation is the practicality of the experimental setup itself, which is extremely inflexible, acting as a barrier to real-world implementation. A related study [17] showcased a system that uses a stereo camera and a high-speed, high-precision semantic segmentation model. With the proposed system, authors could estimate relative speeds by measuring changes in distance and time differences between frames. The proposed approach adds value due to its segmentation methodology, which captures more information than the one-stage object detectors. In similar research [18], the authors developed an experimental setup with small vehicles to test the accuracy of a preexisting model that estimates vehicle speeds. The speed calculations are validated by comparing the measurements obtained with the reference measurements recorded from an infrared sensor. The experimental results also provided insights to the frame-skipping threshold to reduce the processing time of the overall footage--a direction toward real-time implementation. The authors planned to test this system on real vehicular traffic. Optical occlusions are a barrier to the real-world implementation of vision-based systems. [PERSON] and [PERSON] [19] tried to address this problem by creating an experimental setup that utilizes multiple cameras positioned at different angles, coupled with a complex 3D convolutional neural network (CNN) architecture. The study yielded promising results, paving the way for view-invariant vehicle speed measurement systems. You Only Look Once (YOLO) is a single-stage object detection algorithm that has received widespread attention in various fields and holds tremendous potential for traffic monitoring tasks. In a study by [PERSON] and [PERSON] [20], the authors used the YOLOv5 for vehicle detection and tracking, while employing an RNN for speed estimation. The proposed system achieved an error rate of 4.08 km/h, significantly lower than the acoustic, sensor-based measurements. The authors further discussed the prospects of a multimodal system--combining audio and video data to improve accuracy. Similar to fixed camera systems, researchers have also explored the prospects of combining UAVs with intelligent systems to estimate vehicle tracks and speeds. In a study by [PERSON] and [PERSON] [21], the potential of UAVs in RTM systems was explored. The experiment was conducted by collecting and analyzing traffic footage taken from varying altitudes and resolutions and implementing a YOLO architecture for detection and tracking. The study also discusses the limitations faced by nonstationary camera systems, which includes camera calibration resulting in inconsistencies in speed estimations. The proposed method achieved an accuracy of 1.1 km/h, which is remarkable but, like other research works, is implemented only on a stationary camera or UAV. To develop a practically viable, vision-based RTM system, scientists have been exploring the right balance between accuracy and computational efficiency.