Most of the existing solutions predicting visual attention focus solely on referenced 2D images and disregard any depth information. This aspect has always represented a weak point since the depth is an inseparable part of the biological vision. This paper presents a novel method of saliency map generation based on results of our experiments with egocentric visual attention and investigation of its correlation with perceived depth. We propose a model to predict the attention using superpixel representation with an assumption that contrast objects are usually salient and have a sparser spatial distribution of superpixels than their background. To incorporate depth information into this model, we propose three different depth techniques. The evaluation is done on our new RGB-D dataset created by SMI eye-tracker glasses and KinectV2 device.

Date Published: 17 March 2017
PDF: 8 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103410T (17 March 2017); doi: 10.1117/12.2268617

Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)