## 6 Experiment and Results
### Target-Tracking Experiments  
Experiment 2: The purpose of this experiment was to verify the comprehensive performance of the target-tracking algorithm combined with the FDA-SSD detection model for moving target recognition, positioning, and tracking under the static conditions of the camera. In this experiment, the monocular camera was fixed, and the circular grid target moved randomly indoors. The algorithm calculated the spatial-trajectory coordinates of the output target in real-time, which were compared with the measured values of the motion \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mAP** & **FPS** & **Boxes** & **GPU** & **Input Resolution** \\ \hline YOLO v1 & 97.7 & 21.2 & 98 & GTX1060 & 448 \(\times\) 448 \\ YOLO v2 & 98.9 & 27.4 & 1521 & GTX1060 & 416 \(\times\) 416 \\ SSD & 98.6 & 24.3 & 8732 & GTX1060 & 300 \(\times\) 300 \\ FDA-SSD & 98.3 & 28.7 & \(\sim\)dynamic & GTX1060 & 300 \(\times\) 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of detection models based on neural network in experiment 1. Figure 12: Example when using FDA-SSD for target detection: (**a,b**): Circular-target detection under different depth conditions; (**c,d**): rectangle-target detection under different depth conditions; and (**e,f**): classification of moving monochrome targets. Experiment 3: The purpose of this experiment was to verify the comprehensive performance of the target-tracking algorithm combined with the FDA-SSD detection model for moving target recognition, positioning, and tracking under the condition of camera motion. In this experiment, the monocular camera faced the tracking target and moved randomly, and the rectangular solid-color target moved randomly indoors. The algorithm output the space trajectory coordinates of the target in real-time, which was compared with the measured value of the motion-capture system. Compared with the conditions of the camera in the second experiment, the motion of the monocular camera in this experiment often brought image jitter, resulting in higher requirements for detection. Additionally, the trajectory of the camera was provided by the indoor motion-capture system. The following Figures 14 and 15 show the results of related experimental data. Figure 14: Pose of the camera obtained by motion\(-\)capture system. Figure 15: _Cont_. Table 3 shows the root mean square error of dynamic target tracking in experiment 3:  
The data in Figures 14 and 15 and Table 3 indicate that our mono-visual target-tracking framework in this study can well realize the dynamic detection and tracking of the in-door target by using the FDA-SSD model. Compared with the ground truth of the motion-capture system, the actual tracking RMSE of the tracking algorithm on the three coordinate axes x, y, and z was less than 4.72 cm. The overall tracking process was stable, and there was no tracking failure. Experiment 3 reflects that the algorithm in this study can still effectively realize the detection and 3D tracking of moving targets on the dynamic detection platform. We also analyzed the computational timing performance of the object-tracking experiments in Experiment 3. We conducted three sets of localization experiments, using YOLO v2 [28], SSD, and FDA-SSD to test the same samples in the OTMP dataset. Considering that the solution of the camera pose in this study was given by the indoor positioning system, we focused on analyzing the timing performance of the detection module and the spatial positioning module, where the Timing for Detection refers to the time-consuming of the neural-network-based detection model; the Timing for Location refers to the time-consuming of the 3D-positioning algorithms. It can be seen from Table 4 that the 3D-positioning algorithm used in our framework takes only less than 3 ms, which only takes up very little computing resources compared to the detection model. Therefore, improving the operation speed of the detection model is particularly important to save the memory and computing resources of the entire system.