Video Salient Object Detection via Fully Convolutional Networks

Wang, Wenguan, Shen, Jianbing and Shao, Ling (2018) Video Salient Object Detection via Fully Convolutional Networks. IEEE Transactions on Image Processing, 27 (1). pp. 38-49. ISSN 1057-7149

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Abstract

This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the densely annotated video segmentation data set (MAE of .06) and the Freiburg-Berkeley Motion Segmentation data set (MAE of .07), and do so with much improved speed (2 fps with all steps).

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: Pure Connector
Date Deposited: 24 Oct 2017 05:09
Last Modified: 27 Oct 2020 00:51
URI: https://ueaeprints.uea.ac.uk/id/eprint/65221
DOI: 10.1109/TIP.2017.2754941

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