  Despite the growing number of unmanned aerial vehicles (UAVs) and aerial
videos, there is a paucity of studies focusing on the aesthetics of aerial
videos that can provide valuable information for improving the aesthetic
quality of aerial photography. In this article, we present a method of deep
multimodality learning for UAV video aesthetic quality assessment. More
specifically, a multistream framework is designed to exploit aesthetic
attributes from multiple modalities, including spatial appearance, drone camera
motion, and scene structure. A novel specially designed motion stream network
is proposed for this new multistream framework. We construct a dataset with
6,000 UAV video shots captured by drone cameras. Our model can judge whether a
UAV video was shot by professional photographers or amateurs together with the
scene type classification. The experimental results reveal that our method
outperforms the video classification methods and traditional SVM-based methods
for video aesthetics. In addition, we present three application examples of UAV
video grading, professional segment detection and aesthetic-based UAV path
planning using the proposed method.
