## 2 Related Works
### Data Collection Stage  
At this stage, a method is used to collect 360-degree video control data based on end-to-end control in order to provide an NFoV of the objects, as shown in Figure 2. When using end-to-end control, the user can intuitively set the NFoV with the objects. The user views the 360-degree video and controls it in the direction of the objects, checks the 360-degree image included in the 360-degree video and checks the NFoV by maneuvering in the direction of the objects. The Video Control Signal \(s_{i,t}\) and Image \(v_{i,t}\) generated in this process are collected as part of the 360-degree video control data. NFoV \(n_{i,t}\) is provided when Image \(v_{i,t}\) is controlled by Video Control Signal \(s_{i,t}\).  
Figure 1: In the process of micro unmanned aircraft systems (micro UAS) autonomous flight, a 360-degree video captured by an omnidirectional camera is used to provide a normal field of view (NFoV) to the user.  
The preprocessing method using the collected 360-degree video control data is shown in Figure 3. It is a pseudocode for generating preprocessed 360-degree video control data through preprocessing based on 360-degree video control data as shown in Algorithm 1. In the defined video control signal extraction step, representative video control signals are extracted; if all values that can be represented by the video control signals are used, the representative \(\kappa\) video control signals are used to address the increased complexity. The specified \(\kappa\) video control signals are defined as defined video control signals \(s^{\prime}{}_{\kappa}\), composed of video control signals that, like Video Control Signal \(s_{i,t}\), control 360-degree video. The video control signals collected by the K-means algorithm [18] are analyzed to extract the defined video control signals. The \(\kappa\) video control signals are designated to control the NFoVs in which the objects can be placed, and the defined video control signals are set using the results of classification by the K-means algorithm using the collected video control signals, as shown in Equation (1).  
\[\arg\min_{S^{\prime}}\sum_{j=1}^{\kappa}\sum_{S_{i,t}\in S}\|S_{i,t}-S^{\prime} \|^{2} \tag{1}\]  
where \(S\) is \(\{S_{1},\,S_{2},\,\ldots\,S_{i},\,\ldots\,\}\) and \(S^{\prime}\) is \(\{S^{\prime}{}_{1},\,S^{\prime}{}_{2},\ldots\,S^{\prime}{}_{\kappa}\}\)  
Video control signals encoding is performed to input video control signals included in 360-degree video control data to a deep learning model using defined video control signals. The labeling of \(S_{i}\) is defined as a Control Label Set \(L_{i}\), described by \([l_{i,1},l_{i,2},\,...,\,l_{i,t},\,...\,\}\). Control Label \(l_{i,t}\) is the index of \(s^{\prime}{}_{\kappa}\). Video Control Signal \(s_{i,t}\) finds the nearest defined video control signal with the Euclidean distance and sets the Control Label \(l_{i,t}\), as shown in Equation (2). The end-to-end control-based deep learning model is learned using Video \(V_{i}\) and Control Label Set \(L_{i}\).  
\[l_{i,t}=\arg\min\limits_{j}EuclideanDistance\Big{(}S_{i,t},\,\,S^{\prime}{}_{j} \Big{)} \tag{2}\]  
Figure 3: Preprocessing to input 360-degree video control data into the deep learning model.  
Figure 2: Process of generating 360-degree video control data that provides an NFoV using a 360-degree image and a video control signal.