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首页> 外文期刊>Journal of visual communication & image representation >Salient object detection in video using deep non-local neural networks
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Salient object detection in video using deep non-local neural networks

机译：使用深非局部神经网络的视频中突出的对象检测

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摘要

Detection of salient objects in image and video is of great importance in many computer vision applications. In spite of the fact that the state of the art in saliency detection for still images has been changed substantially over the last few years, there have been few improvements in video saliency detection. This paper proposes a novel non-local fully convolutional network architecture for capturing global dependencies more efficiently and investigates the use of recently introduced non-local neural networks in video salient object detection. The effect of non-local operations is studied separately on static and dynamic saliency detection in order to exploit both appearance and motion features. A novel deep non-local fully convolutional network architecture is introduced for video salient object detection and tested on two well-known datasets DAVIS and FBMS. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods. (C) 2020 Elsevier Inc. All rights reserved.
机译：在许多计算机视觉应用程序中检测图像和视频中的突出对象非常重要。尽管仍然在过去几年中静止图像的显着性检测状态的静止图像的状态发生了改变，但视频显着性检测已经很少有所改善。本文提出了一种新的非本地全卷积网络架构，用于更有效地捕获全局依赖性，并调查在视频突出对象检测中最近引入的非局部神经网络的使用。非本地操作的效果在静态和动态显着性检测上分别研究，以利用外观和运动功能。引入了一种新的非本地完全卷积网络架构，用于视频突出对象检测并在两个众所周知的数据集DAVIS和FBM上进行测试。实验结果表明，该算法优于最先进的视频显着性检测方法。 （c）2020 Elsevier Inc.保留所有权利。

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