# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 5 Discussion  
The detection rates of Video 1 and 2 are 92% and 89%, respectively. The detection rates were affected by simulating harsh conditions that the frames were gray-scaled and resized by 50%, and the frame rate was modified to 1/3 of the original one. Missing target detection degrades tracking performance; a long interval of missing targets may cause the track to be terminated or segmented. Using full color images at the original resolution and frame rate can increase the detection performance. A few false alarms are detected except for targets of no interest (pedestrians). Typically, data association suffers from high clutters, low detection, state and measurement errors, and closely located targets. The NN is the most effective in computing and requires no initial assumptions on the number and states of targets. Indeed, the measurement characteristics in this study makes it possible to use the NN association as follows: (1) the target is transformed from the visual shape in the image frame into a point object in a Cartesian coordinate system during object detection, thus the measurement is not a continuous value, but a discrete 2D signal. The measurement resolution is the same with the spatial resolution of the image. Therefore, no false alarm or other targets can exist around the point of the true target depending on the physical shape of the target and the spatial resolution. In other words, as long as the validation region falls inside the target body, there is no possibility of the association with false alarms or other targets. In consequence, the accuracy of position estimates more matters than data association strategies. It is noted that the proposed track association scheme increases the accuracy of the target state in position as well as eliminating redundant tracks, (2) the false alarm rate is very low, and its existence is limited in space. Most false measurements were caused by buildings and some were caused by parked cars except for the non-target objects (pedestrians) in the experiments, so it is effective to use the NN association. The NCV model is adopted in the experiments. The NCV, nearly constant acceleration (NCA), and coordinated turn (CT) models are most commonly used among various state-space dynamic models [35; 36]. The choice of the model depends on the target's maneuver, the measurement quality, and the measurement rate [36]. With high quality measurements, the NCA model can produce the accurate estimate of the acceleration. The CT model is often used for targets in horizontal planes and extended to 3D space [34]. We leave the \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** \\  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Previous & 0.95 & 1.27 & 1.36 & & 0.89 & 1.85 & 2.39 & & 1.05 & & & & 1.89 & \\ Proposed & 1.95 & 1.32 & 1.65 & & 1.68 & 2.31 & 5.97 & & 0.91 & 1.12 & 0.95 & 1.76 & 0.81 \\ \hline  & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** & **Target** \\  & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** & **21** & **22** & **23** & **Avg.** \\ \hline Previous & 1.18 & 2.66 & 1.10 & 1.15 & & 1.49 & 1.14 & 0.99 & 0.89 & 1.12 & 1.54 & 4.28 & 1.65 \\ Proposed & 2.17 & 4.03 & 1.30 & 1.31 & & 2.29 & & 1.43 & 3.10 & 1.74 & 4.15 & 1.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Velocity RMSE of Video 2 (m/s).