# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
## 4 Results and Discussion
The added movements, along with georeferencing errors, can notably influence the precision of the vehicle tracks, potentially leading to exaggerated velocity measurements (see Figure 7). This problem is not resolved with a low-pass filter. Instead, a distance-based movement threshold was implemented to decrease positional inaccuracies and, consequently, refine the velocity measurements. While this approach does introduce a certain level of discretization in the output, it is a solution aimed at enhancing the overall accuracy of vehicle tracking in nonstationary UAV footage. This limitation, however, also opens up a valuable opportunity for further research. It highlights the need for innovative solutions that can improve the positional accuracies in nonstationary UAV footage without the discretization of valuable information. In the absence of a reference vehicle position, the buffer overlay method can be used to measure the accuracy of mapped vehicle trajectories. A vector path of the actual vehicle path was drawn manually, considering the target vehicle's position with respect to time, and a buffer of 1 m was constructed (illustrated in Figure 8). Then, the tracks generated by the proposed method were compared with the ground-truthing buffer, calculating the total length inside the buffer. Figure 5: The fluctuations in velocity (in km/h) over time (in seconds) and the removal of errors using an EMA-based low-pass filter (\(\alpha\) = 0.1). The single-point reference speed measured by the speed gun was 26 km/h. Figure 6: The pseudo tracks generated by the object tracking algorithm due to UAV movement. In the comparative analysis, it was observed that positional accuracies depend on the speeds of the UAVs. The tracks extracted from nonstationary drone footage were notably accurate and consistent. However, tracks obtained from nonstationary footage displayed minor positional inaccuracies, which tended to increase with the optical destabilization caused by higher UAV speeds. For example, track 09 was 81% inside the 1 m buffer, and track 13 was 61% inside the buffer, as detailed in Table 4.  
Figure 8: The method used for determining the positional accuracies of vehicle tracks on (**a**) tracks from stationary drone footage and (**b**) tracks from moving drone footage. Figure 7: Extreme velocity (km/h) over time (s) with fluctuation resulting from pseudo tracks and their removal from the distance-based movement threshold (after introducing the distance threshold, the first measurement starts at 4.3 s). The single-point reference speed measured by the speed gun was 26 km/h. The computational expense of processing nonstationary drone footage is a significant limitation. On a system with an Intel(r) Core(tm) i9-9900 CPU @ 3.60 GHz and 64 GB RAM, the average processing time for nonstationary drone footage was 63 s per frame, compared with just 0.42 s for stationary drone footage. The longer processing time for nonstationary footage is due to the computationally expensive SIFT application for each frame. However, this can be reduced by 8% through the use of precalculated key points and descriptors. Georeferencing also causes errors, resulting in jumpy locations across frames, requiring aggressive noise removal methods, such as movement threshold. Nonetheless, the proposed system can accurately estimate vehicle speed and position. Future research will be focused on computational optimization techniques and innovative data-denoising methods for improving output quality in nonstationary drone footage.