# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 4 Results  
### Video Description  
Videos 1 and 2 were captured by a DJI Phantom 4 and Inspire 2 in urban environments, respectively. The drones flew at the height of 150 m in a straight line in Video 1 [8] and changed direction once in Video 2. The videos were captured in different weather and lighting conditions. The drone's speed was set to be constant on the controller by the operator. The camera was pointed directly at the ground and captured video clips at 30 fps for 15 and 24 s in Videos 1 and 2, respectively. It is processed every third frame for efficient image processing, so a total of 151 and 242 frames are considered in each video and the actual frame rate is 10 fps. The original frame size of Video 1 is 4096 \(\times\) 2160 pixels, but the frame is gray-scaled and resized by 50% to reduce the computational time. In Video 2, the original frame of 3840 \(\times\) 2160 pixels is scaled the same way. One pixel corresponds to 0.11 m after resizing. There are 9 moving vehicles (6 cars, 2 buses, 1 bike) and several pedestrians in Video 1 while there are 23 moving vehicles (18 cars, 2 buses, 2 motorcycles, 1 bicycle) in Video 2. The details of the videos are described in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Video 1** & **Video 2** \\ \hline Multicopter/Camera & Phantom 4/Bundle & Inspire 2/Zemnuse X5 \\ Flying speed (m/s) & 5.1 & 5 \\ Flying time (sec) & 15 & 24 \\ Flying direction & West\(\rightarrow\)East & West\(\rightarrow\)East\(\rightarrow\)North \\ Actual fanze number & 151 & 241 \\ Actual frame size (pixels) & 2048 \(\times\) 1080 & 1920 \(\times\) 1080 \\ Actual frame rate (fps) & & 10 \\ Number of moving vehicles & 9 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Video descriptions.  
Figure 4: Illustration of the track association process, (**a**) two tracks \(s\) and \(t\); (**b**) track association hypothesis testing; (**c**) track selection and fusion.  
Figure 5: Illustration of track life and track termination criteria.  
The average detection rate of Video 1 is 92%. A total of 23 false alarms were detected including 16 moving pedestrians, but the pedestrians were not considered targets of interest in this study. The average detection rate of Video 2 is 89%. There were 47 false alarms, of which 4 moving pedestrians were detected. All the centroids of the object windows including false alarms of Video 1 and 2 are shown in Figures 10 and 11, respectively, in the expanded frame. The number of detections is 709 and 1016 for each video, respectively. The expanded frame of Video 1 is 2779 \(\times\) 1096 pixels, equivalent to 305.7 \(\times\) 120.6 m; Video 2 is 2641 \(\times\) 1426 pixels, equivalent to 290.5 \(\times\) 156.7 m. The position coordinates of the expanded frame are compensated by \(\left[\hat{p}_{x}(k)\ \hat{p}_{y}(k)\right]\) obtained in Equation (2). It is noted that the center locations are input to the next target tracking stage as measurements.  
Figure 8: Video 1: moving object detection of Figure 6b, (**a**) frame registration and subtraction; (**b**) morphological filtering; (**c**) removing false alarms; (**d**) detection results (bounding boxes).  
Figure 9: Video 2: moving object detection of Figure 7b, (**a**) frame registration and subtraction; (**b**) morphological filtering; (**c**) removing false alarms; (**d**) detection results (bounding boxes).