# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
## 5 Conclusions  
This study demonstrates the potential of combining artificial intelligence (AI) and unmanned aerial vehicles (UAVs) to improve road traffic monitoring systems, specifically in estimating vehicle speed and trajectory--a novel method using advanced feature matching and deep learning techniques alongside UAV technology. The experimental findings confirm that UAV-based systems equipped with AI can overcome many limitations of existing RTM systems and provide more accurate speed measurements compared with point-based estimations. The proposed system offers near-real-time speed when applied to stationary drone footage; although, there is a trade-off in processing speed with dynamic drone footage. Improving the processing speed could make the system more scalable in all cases. Drones' ability to provide a mobile aerial perspective adds a valuable dimension to traffic analysis, offering more comprehensive coverage and detail. Moreover, the use of AI for automating vehicle detection and tracking has been shown to reduce the need for manual intervention, making the process more efficient and accurate. This advancement is crucial for practically feasible RTM systems, where swift and accurate data analysis and insights are essential. Despite the promising results, the study acknowledges the inherent challenges of developing a system that is both efficient and fully adaptable to real-world conditions. UAV-based operations are only feasible during clear daylight hours and cannot be conducted at night or in extreme weather situations. Additionally, the range limitations of drones and battery life restrict perpetual flight, meaning this system should be viewed as a supplementary solution to ground-based, fixed camera systems. The aerial perspective provided by drones offers significant advantages, such as covering larger areas and enhanced maneuverability. Integrating a UAV-based RTM system can yield substantial benefits. Given the accuracy of the measurements that the system can provide, it adds considerable value. This method is particularly useful for short-term traffic monitoring in potential conflict zones and helping to understand road user behavior. By analyzing this behavior from an aerial perspective, life-saving safety interventions can be implemented; however, environmental factors, such as bird migration routes, must be considered during aerial surveillance. Future research will focus on refining this system by incorporating multisource data, including ground and aerial surveillance footage, for more comprehensive analysis. Furthermore, efforts will be made to enhance processing speeds \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Track** & **Setting** & **UAV Speed** & **Track Length** & **Track Length (m)** \\  & & **(m/s)** & **(m)** & **(Inside Buffer) \({}^{*}\)** \\ \hline
02 & Stationary & - & 52 & 52 \\
09 & Nonstationary & 05 & 93 & 76 \\
13 & Nonstationary & 10 & 128 & 78 \\ \hline \hline \end{tabular}  
* Ground truthing buffer has a width of 1 m. Higher buffer widths are attributed to relaxation in the evaluation criterion.  
\end{table} 
Table 4: Comparative analysis of the positional accuracy of the vehicle tracks in three different drone settings. and to implement methods that prevent data loss caused by the current error removal techniques used in nonstationary drone experiments. In conclusion, this work demonstrates the significant advantages of using UAVs and AI in road traffic monitoring, representing a step forward in the pursuit of safe and efficient transportation systems. As technology advances, integrating these smart systems holds the promise of revolutionizing how we understand and manage road traffic, ultimately contributing to better, more responsive urban environments. Conceptualization, M.W.A. and W.E. ; methodology, M.W.A. and W.E. ; software, M.W.A. ; validation, W.E. ; formal analysis, M.W.A. ; investigation, M.W.A. and W.E. ; resources, W.E., M.A. ([PERSON]), D.J. and G.W. ; data curation, M.W.A. and W.E. ; writing--original draft preparation, M.W.A.