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Hierarchical segment learning method for road objects extraction and classification

conference contribution
posted on 06.12.2017, 00:00 by Tejy Kinattukara JobachanTejy Kinattukara Jobachan, Brijesh VermaBrijesh Verma
In this paper, we propose a new hierarchical segment learning approach for extraction and classification of roadside objects. The proposed approach is based on hierarchical segment extraction and classification of segmented objects using a neural network. In this approach, we extract different road objects such as sky, road, sign and vegetation in hierarchical stages and classify them using a neural classifier. The approach improves the overall classification accuracy while extracting different road objects from the road images. The proposed approach has been applied to a set of images extracted from video data collected by Transport and Main Roads Queensland. The experimental results indicate that this approach can extract and classify road objects with a reasonable high accuracy.

Funding

Category 3 - Industry and Other Research Income

History

Start Page

432

End Page

438

Number of Pages

7

Start Date

01/01/2013

Finish Date

01/01/2013

Location

Sydney, Australia

Publisher

IEEE Computer Society

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Institute for Resource Industries and Sustainability (IRIS); School of Engineering and Technology (2013- );

Era Eligible

Yes

Name of Conference

IEEE International Conference on Computational Science and Engineering