Learning Gaze Transitions From Depth to Improve Video Saliency Estimation

George Leifman, Dmitry Rudoy, Tristan Swedish, Eduardo Bayro-Corrochano, Ramesh Raskar; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1698-1707

Abstract


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.

Related Material


[pdf] [Supp] [arXiv]
[bibtex]
@InProceedings{Leifman_2017_ICCV,
author = {Leifman, George and Rudoy, Dmitry and Swedish, Tristan and Bayro-Corrochano, Eduardo and Raskar, Ramesh},
title = {Learning Gaze Transitions From Depth to Improve Video Saliency Estimation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}