Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
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Leifman_Learning_Gaze_Transitions_ICCV_2017_paper.pdf
Description
Published version
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1.19 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Leifman, George
Rudoy, Dmitry
Swedish, Tristan
Bayro-Corrochano, Eduardo
Raskar, Ramesh
Date Issued
October 2017
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Leifman, George, Rudoy, Dmitry, Swedish, Tristan, Bayro-Corrochano, Eduardo and Raskar, Ramesh. 2017. "Learning Gaze Transitions from Depth to Improve Video Saliency Estimation."
Version
Final published version
Abstract
© 2017 IEEE. In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing videos that contain a depth map (RGBD) on a 2D screen. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired yet hard to display. Despite considerable progress in 3D display technologies, most are still expensive and require special glasses for viewing, so RGBD content is primarily viewed on 2D screens, removing the depth channel from the final viewing experience. We train a generative convolutional neural network that predicts the 2D viewing saliency map for a given frame using the RGBD pixel values and previous fixation estimates in the video. To evaluate the performance of our approach, we present a new comprehensive database of 2D viewing eye-fixation ground-truth for RGBD videos. Our experiments indicate that it is beneficial to integrate depth into video saliency estimates for content that is viewed on a 2D display. We demonstrate that our approach outperforms state-of-the-art methods for video saliency, achieving 15% relative improvement.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/iccv.2017.188
