## 4 Discussion  
As illustrated in Figure 14b, the false alarms of the action recognition were usually caused by similar actions. One solution to this problem is to select completely different actions as the features in the code book for different flight commands. Moreover, for the gesture recognition, Figure 25 indicates that false alarms arose in all of the compared algorithms. In our system, the false alarm was caused by the camera viewpoint, in which \"Vertical Knife\" resembled \"Vertical Palm\". The other compared algorithms usually produced false alarms on the background component or were affected by another person (or even parts of their own body).  
According to Tables 5 and 6, the recall rate of our method changed significantly. Such variations in the system performance are caused by the noise in the dataset. As illustrated in Figure 26a,b, the depth image in the NYU dataset often contained other noise information such as the arm or wrist, and the segmented hand region ((c) and (d)) in our dataset was usually clear and easily recognized. This phenomenon is quite natural because better segmented images generally lead to improved recognition results. As for the accuracy loss in other algorithms like YOLO-V3, we consider that is caused due to the following reason: (1) less training samples (2000 samples) in our dataset (52,360 training samples in NYU dataset); (2) there is no background objects in NYU dataset (as shown in Figure 26a,b), but our dataset contains complex background conditions such as: indoor and outdoor, various background components and strong illumination changes (as shown in Figures 25 and 27).  
Figure 24: Experimental results of controlling the flight of a real UAV with the proposed system. (**a**) Under the real urban scene; (**b**) Under the strong illumination condition.  
Figure 23: Simulation experimental result of our system when multiple person perform similar actions.  
The processing speeds of each module in our system were as follows: operation module: 30  ms, action recognition module: 45 ms, gesture recognition module: 15 ms, scene understanding module: 2 ms, and cross-validation: 5 ms. Therefore, the total processing speed of our system was approximately 97 ms, and the signal delay between the PC and UAV was less than 0.1 s.  
During our experiments on the real UAV platform, the working distance between the UAV and pilot was approximately 2 to 800 m (theoretically, a maximum of 2 km) with no occlusion. We tested  
Figure 27: Complex background conditions of our dataset. (**a–d**) Including indoor, outdoor, multiple persons and cluttered background.  
Figure 25: Examples of false alarms arised in all compared algorithms in our test dataset. The yellow rectangle refers to the correct recognition; the red one means the false alarm (solid line) or miss detection (dot line).  
Figure 26: The noise in test dataset may degrade the performance of gesture recognition algorithms. (**a,b**) are the original depth image and corresponding binarized image in NYU dataset; (**c,d**) are the input image and segmented hand region in our dataset.  
our UAV under three scenes, namely wood, urban, and factory, and the maximum wind speed was approximately 6.5 m/s during the test.