Autonomous navigation and sign detector learning

Ellis, L, Ofjall, K, Hedborg, J, Felsberg, M, Pugeault, N and Bowden, R (2013) Autonomous navigation and sign detector learning In: 2013 IEEE Workshop on Robot Vision (WORV 2013), 2013-01-15 - 2013-01-17, Clearwater Beach, Florida.

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Abstract

This paper presents an autonomous robotic system that incorporates novel Computer Vision, Machine Learning and Data Mining algorithms in order to learn to navigate and discover important visual entities. This is achieved within a Learning from Demonstration (LfD) framework, where policies are derived from example state-to-action mappings. For autonomous navigation, a mapping is learnt from holistic image features (GIST) onto control parameters using Random Forest regression. Additionally, visual entities (road signs e.g. STOP sign) that are strongly associated to autonomously discovered modes of action (e.g. stopping behaviour) are discovered through a novel Percept-Action Mining methodology. The resulting sign detector is learnt without any supervision (no image labeling or bounding box annotations are used). The complete system is demonstrated on a fully autonomous robotic platform, featuring a single camera mounted on a standard remote control car. The robot carries a PC laptop, that performs all the processing on board and in real-time. © 2013 IEEE.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Ellis, L Ofjall, K Hedborg, J Felsberg, M Pugeault, N Bowden, R
Date :	2013
DOI :	10.1109/WORV.2013.6521929
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/PBL IEEE,
Additional Information :	© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User :	Symplectic Elements
Date Deposited :	17 Sep 2013 10:49
Last Modified :	31 Oct 2017 15:11
URI:	http://epubs.surrey.ac.uk/id/eprint/797498

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