Learning Gaze Transitions from Depth to Improve Video Saliency Estimation
| Autor: | Tristan Swedish, Ramesh Raskar, Eduardo Bayro-Corrochano, Dmitry Rudoy, George Leifman |
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| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Pixel business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Stereo display Convolutional neural network Gaze Visualization Depth map Fixation (visual) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Saliency map Computer vision Artificial intelligence business |
| Zdroj: | ICCV |
| DOI: | 10.48550/arxiv.1603.03669 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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