Depth-based fusion network for human attention prediction in 3D light field

Ziling Wen; Xinzhu Sang; Binbin Yan; Peng Wang; Duo Chen

doi:10.1117/12.2606165

24 November 2021 Depth-based fusion network for human attention prediction in 3D light field

Ziling Wen, Xinzhu Sang, Binbin Yan, Peng Wang, Duo Chen

Proceedings Volume 12066, AOPC 2021: Micro-optics and MOEMS; 1206612 (2021) https://doi.org/10.1117/12.2606165
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

The human visual attention mechanism promotes human to acquire the most important cues from large amount of information. However, most methods of simulating human visual attention now focus on 2D display, people know little about how human assign their visual attention under a 3D display. This paper firstly produced a saliency dataset consisting of human eye-fixation data for different 3D scenes under human quick glance, which demonstrated the human visual attention distribution. By analyzing the dataset, an approach based on convolutional neural network for human visual attention prediction under 3D light field display was proposed. The network is composed of three parts which are two-way feature extraction, feature fusion and prediction output. Comparing with saliency prediction models under 2D display devices, our proposed model can predict the distribution of human visual attention under 3D light field more accurately. This research promote further investigation of 3D applications such as 3D device evaluation and 3D content production.

Citation Download Citation

Ziling Wen, Xinzhu Sang, Binbin Yan, Peng Wang, and Duo Chen "Depth-based fusion network for human attention prediction in 3D light field", Proc. SPIE 12066, AOPC 2021: Micro-optics and MOEMS, 1206612 (24 November 2021); https://doi.org/10.1117/12.2606165

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available