## 1 Introduction  
Micro unmanned aircraft systems (micro UAS) [1] equipped with a camera [2; 3; 4] have been used in applications such as traffic surveillance and hobby filming. In addition, data collected from the micro UAS is used to perform real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, precision agriculture, and civil infrastructure inspection for various civil applications [5; 6; 7]. Micro UAS has the potential to revolutionize the science, practice, and role of remote sensing in a variety of applications [8]. As micro UAS technology advances, providing various applications for user convenience is possible. To perform a task, the micro UAS flies autonomously based on the pilot's control or the waypoint set by the pilot [9; 10; 11]. During the flight, the camera mounted on the micro UAS captures its surrounding environment. To photograph or track an object for the micro UAS task, it shoots autonomously as well as through a camera attached to the micro UAS [12; 13; 14]. The micro UAS controls its direction to photograph or track the objects, or the camera attached to the micro UAS controls its controllable gimbal in the direction of the objects to be photographed or tracked. When an omnidirectional camera is attached to the micro UAS, the surrounding environment can be photographed without controlling the gimbal. Using the micro UAS equipped with an omnidirectional camera, the surrounding environment is photographed. The user controls the generated 360-degree video in the direction to be viewed and confirms the objects. The 360-degree video can be automatically controlled based on the objects captured [15; 16; 17]; during this process, we require methods to recognize the objects as well as to control the 360-degree video by prioritizing objects. Normal field of view (NFoV) [17] refers to a view presented as an image to a user in a 360-degree video. A problem encountered when tracking one or more objects is that a method is required to provide the NFoVs in a 360-degree video without specifying the objects in it and to allow the user to intuitively view the 360-degree video and specify the NFoVs. To provide NFoVs, the 360-degree video can be intuitively controlled via the end-to-end control method [17]. The video control signal used herein is the signal that provides NFoV in a 360-degree video. The number of video control signals and video control signals provided to the user is specified by the user, and NFoV is provided using the video control signals inferred based on the images contained in the 360-degree video. If the object you want to observe is on the edge of the NFoV, the screen will change frequently and cause dizziness. There may also be unused video control signals if the user directly sets the video control signals to control 360-degree video. Therefore, there is a need for a method for automatically determining video control signals suitable for 360-degree video. This paper proposes a method of automatically generating representative video control signals to automatically control 360-degree video for micro UAS controllers. The user views the 360-degree video, maneuvers it in the direction of the objects, and collects the resulting 360-degree video and video control signals. The video control signals to be used as the output of end-to-end control-based deep learning are automatically generated by classifying the collected video control signals through the K-means algorithm [18]. A method for training end-to-end control-based deep learning model is proposed using generated video control signals. Based on the collected 360-degree video and generated video control signals, the deep learning model learns to control the 360-degree video. The trained deep learning model is used to control the 360-degree video, and the user is provided with the NFoVs. In this approach, NFoVs are automatically provided by the captured 360-degree video; the generated video control signals are set based on the purpose of the video.