We present ongoing work to harness biological approaches to achieving highly efficient egocentric perception by combining the space-variant imaging architecture of the mammalian retina with Deep Learning methods. By pre-processing images collected by means of eye-tracking glasses to control the fixation locations of a software retina model, we demonstrate that we can reduce the input to a DCNN by a factor of 3, reduce the required number of training epochs and obtain over 98% classification rates when training and validating the system on a database of over 26,000 images of 9 object classes.
@article{arxiv.1809.01633,
title = {Efficient Egocentric Visual Perception Combining Eye-tracking, a Software Retina and Deep Learning},
author = {Nina Hristozova and Piotr Ozimek and Jan Paul Siebert},
journal= {arXiv preprint arXiv:1809.01633},
year = {2018}
}
Comments
Accepted for: EPIC Workshop at the European Conference on Computer Vision, ECCV2018