## 2 Proposed System
### Gesture Recognition Module  
As illustrated in Figure 4b, even if training takes place with massive samples, false alarms may still arise owing to the complex background conditions or the training samples not being able to cover all possible variations in human skin color and shape, among others. Moreover, different people may perform the same gesture in varying manners as a result of their unique habits.  
Based on these considerations, instead of using offline training-based algorithms, we propose an online visual gesture recognition method, in which the personal features of each pilot will be extracted and sorted in his personal feature library before the entire system is operating. The overall processing diagram of this module is presented in Figure 12.  
Within the region of interest (ROI) extracted by the operation detection module, the hand region will be segmented according to the color similarity with the adjustable threshold to follow the variation of illumination and distance. Three kinds of features (expressed as \(\mathbf{x}\) = [\(x_{i}\), i = 1\(\sim\)3]) will be extracted from the ROI as illustrated in Figure 13, where the convex hull and dig angle usually change greatly due to the habit of different person and these feature can not only distinguish hands from other background objects but also from person to person. In this research, six kinds of gestures are applied  
Figure 11: Illustration of the ST-GCN [19], where the spatial temporal graph of a skeleton sequence are used to classify the action of a human.  
Figure 12: Gesture recognition module.  
for controlling the UAV and each one is expressed as (\(g_{j}\), j = 1\(\sim\)6). Therefore, the features of each gesture could be defined as (\(x_{i}^{j}\), i = 1\(\sim\)3, j = 1\(\sim\)6). In the personal feature library, for each pilot, the mean and standard variation of each gesture feature will be expressed as (\(\mu_{i}^{j}(reg)\), \(\sigma_{i}^{j}(reg)\))  
As for the features (\(x_{i}^{(in)}\), i = 1\(\sim\)3) of an input gesture (\(g^{(in)}\)) which is segmented from the hand ROI, a multi-feature cascade classification process is applied to identify its gesture category by the following equations as:  
\[Gesture(ID)=\underset{1<j<6}{\arg\max}\Big{(}P(g_{j}|g^{(in)})\Big{)}\, \tag{3}\]  
\[P(g_{j}|g^{(in)})=P(x_{1}^{j}|x_{1}^{(in)})*P(x_{2}^{j}|x_{2}^{(in)})*P(x_{3} ^{j}|x_{3}^{(in)})), \tag{4}\]  
\[P(x_{i}^{j}|x_{i}^{(in)})=exp^{\frac{-(x_{i}^{(in)}-y_{i}^{j}(reg))^{2}}{ \sigma_{i}^{j}(reg)^{2}}}. \tag{5}\]  
Here, \(P(x_{i}^{j}|x_{i}^{(in)})\) denotes the similarity between \(i_{th}\) feature of an input gesture \(g^{(in)}\) and that of gesture \(g_{j}\) registered in the personal feature library. \(P(g_{j}|g^{(in)}\) represents the similarity from input gesture \(g^{(in)}\) to the predefined gesture \(g_{j}\). The category of \(g^{(in)}\) is obtained by maximizing Equation (3) and such maximum similarity is also beyond a predefined threshold. According to Equation (4), a single low feature similarity between input gesture \(g^{(in)}\) and \(g_{j}\) will lead to its rejection to gesture \(g_{j}\), such process could be considered as the well-known cascade classification procedure. Detailed description about the color segmentation and the computation for (\(\mu_{i}^{j}(reg)\), \(\sigma_{i}^{j}(reg)\)) could be found in [37; 38].