Journal of Visual Communication and Image Representation, Volume (68), Year (2020-4) , Pages (102769-102778)

Title : ( Salient object detection in video using deep non-local neural networks )

Authors: MohammadJafar Shokri , Ahad Harati , Kimya Taba ,

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Abstract

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.

Keywords

, Video saliency detection Deep learning Non, local neural networks Fully convolutional neural networks
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@article{paperid:1083121,
author = {Shokri, MohammadJafar and Harati, Ahad and Kimya Taba},
title = {Salient object detection in video using deep non-local neural networks},
journal = {Journal of Visual Communication and Image Representation},
year = {2020},
volume = {68},
month = {April},
issn = {1047-3203},
pages = {102769--102778},
numpages = {9},
keywords = {Video saliency detection Deep learning Non-local neural networks Fully convolutional neural networks},
}

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%0 Journal Article
%T Salient object detection in video using deep non-local neural networks
%A Shokri, MohammadJafar
%A Harati, Ahad
%A Kimya Taba
%J Journal of Visual Communication and Image Representation
%@ 1047-3203
%D 2020

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