Abstract
Micro unmanned aircraft systems (micro UAS)-related technical research is important because micro UAS has the advantage of being able to perform missions remotely. When an omnidirectional camera is mounted, it captures all surrounding areas of the micro UAS. Normal field of view (NFoV) refers to a view presented as an image to a user in a 360-degree video. The 360-degree video is controlled using an end-to-end controls method to automatically provide the user with NFoVs without the user controlling the 360-degree video. When using the end-to-end controls method that controls 360-degree video, if there are various signals that control the 360-degree video, the training of the deep learning model requires a considerable amount of training data. Therefore, there is a need for a method of autonomously determining the signals to reduce the number of signals for controlling the 360-degree video. This paper proposes a method to autonomously determine the output to be used for end-to-end control-based deep learning model to control 360-degree video for micro UAS controllers. The output of the deep learning model to control 360-degree video is automatically determined using the K-means algorithm. Using a trained deep learning model, the user is presented with NFoVs in a 360-degree video. The proposed method was experimentally verified by providing NFoVs wherein the signals that control the 360-degree video were set by the proposed method and by user definition. The results of training the convolution neural network (CNN) model using the signals to provide NFoVs were compared, and the proposed method provided NFoVs similar to NFoVs of existing user with 24.4% more similarity compared to a user-defined approach.
Highlights
Micro unmanned aircraft systems [1] equipped with a camera [2,3,4] have been used in applications such as traffic surveillance and hobby filming
This paper proposes a method of automatically generating representative video control signals to automatically control 360-degree video for micro UAS controllers
A method is used to collect 360-degree video control data based on end-to-end control, the user can intuitively set the Normal field of view (NFoV) with the objects
Summary
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. When an omnidirectional camera is attached to the micro UAS, the surrounding environment can be photographed without controlling the gimbal. 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. 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 proposed method explores possibilities in terms of collecting training data for the user to intuitively train the deep learning model without tagging objects, and the possibility of automatically providing NFoVs using 360-degree video, regardless of flight. 360-degree videos; Section 4 discusses the experimental results to validate the proposed method; and Section 5 presents the conclusions of this study
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