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首页> 外文期刊>IEEE transactions on multimedia >Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment
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Deep Multimodality Learning for UAV Video Aesthetic Quality Assessment

机译：UAV视频审查的深度多模态学习

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摘要

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.
机译：尽管越来越多的无人驾驶航空公司（无人机）和空中视频，但缺乏对空中视频的美学的缺乏研究，这些研究可以为提高空中摄影的美学质量提供有价值的信息。在本文中，我们提出了一种深度多模态学习的方法，用于无人机视频审查质量评估。更具体地，多级框架框架旨在利用多种模式的美学属性，包括空间外观，无人机相机运动和场景结构。提出了一种新颖的专门设计的运动流网络，用于这个新的多阵线框架。我们构建一个具有由寄生器摄像机捕获的6,000个UAV视频镜头的数据集。我们的模型可以判断UAV视频是否被专业摄影师或业余爱好者与场景类型分类一起拍摄。实验结果表明，我们的方法优于视频分类方法和基于传统的基于SVM的视频美学方法。此外，我们使用所提出的方法展示了UAV视频分级，专业段检测和审美的无人机路径规划的三个应用示例。

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