A Novel Visual Attention Framework using Unsupervised Feature Learning for Road Scene Understanding

DOI

10.2991/icimm-15.2015.215How to use a DOI?

Keywords

Visual attention; Road Scene Understanding; Deep Learning; Bayesian Framework

Abstract

Road scene understanding plays a key role in autonomous driving for intelligent vehicle. For the problem making semantic labeling with equivalent priority results in confliction between huge amounts of data and limited computation resource, this paper proposes a novel framework that efficiently fuses selective visual attention mechanism into the solution to scene perception task. Incorporating top-down and bottom-up two kinds of attention effect into an integrated Bayesian framework, total saliency map can be obtained taking use of implicit feature representation by unsupervised feature learning from natural images.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanfen Mao
AU  - Qingyu Meng
AU  - Ming Chen
PY  - 2015/07
DA  - 2015/07
TI  - A Novel Visual Attention Framework using Unsupervised Feature Learning for Road Scene Understanding
BT  - Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials
PB  - Atlantis Press
SP  - 1201
EP  - 1204
SN  - 2352-5401
UR  - https://doi.org/10.2991/icimm-15.2015.215
DO  - 10.2991/icimm-15.2015.215
ID  - Mao2015/07
ER  -