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arXiv · 2011.02356

Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

Qi Kuang·Xin Jin·Qinping Zhao·Bin Zhou
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Abstract

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.

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Qi Kuang, Xin Jin, Qinping Zhao, Bin Zhou. 2020-11-04. Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment. https://doi.org/10.1109/tmm.2019.2960656

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