Hristozova, N., Ozimek, P. and Siebert, J. P. (2018) Efficient Egocentric Visual Perception Combining Eye-tracking, a Software Retina and Deep Learning. Third International Workshop on Egocentric Perception, Interaction and Computing (EPIC@ECCV18), Munich, Germany, 09 Sep 2018.
|
Text
183354.pdf - Accepted Version 1MB |
Abstract
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 Learn- ing 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% clas- sification rates when training and validating the system on a database of over 26,000 images of 9 object classes.
| Item Type: | Conference or Workshop Item |
|---|---|
| Keywords: | Data efficiency, deep learning, retina, foveated vision, biological vision, egocentric perception, robot vision, visual cortex. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Ozimek, Peter and Siebert, Dr Paul and Hristozova, Nina |
| Authors: | Hristozova, N., Ozimek, P., and Siebert, J. P. |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RZ Other systems of medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| College/School: | College of Science and Engineering > School of Computing Science |
| Research Group: | Computer Vision for Autonomous Systems |
| Copyright Holders: | Copyright © 2018 The Authors |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
| Related URLs: |
University Staff: Request a correction | Enlighten Editors: Update this record
References
References