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An automated system for the analysis of the status of road safety using neural networks

conference contribution
posted on 06.12.2017, 00:00 by Brijesh VermaBrijesh Verma, David Stockwell
This paper presents a neural network based novel automated system that can analyze vehicle mounted video data for improving road safety. There are video data collection systems currently available although no tools exist which could be used to automatically analyze vehicle mounted video data and estimate future crash sites. The main aim of the research presented in this paper is to develop a technique to segment roadside data obtained from vehicle mounted video into regions of interest, classify roadside objects and estimate the risk factor based on roadside conditions and objects for various crashes. A clustering technique for segmentation of roadside frames into regions of interest and a neural network to classify the regions of interest into objects are investigated. The preliminary segmentation and classification results on a small dataset taken from Transport and Main Roads’ vehicle mounted video data collection are promising.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Parent Title

Neural information processing : 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, proceedings. Part 3.

Start Page

530

End Page

537

Number of Pages

8

Start Date

01/01/2011

Finish Date

01/01/2011

ISBN-13

9783642249648

Location

Shanghai, China

Publisher

Springer

Place of Publication

Heidelberg

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Dept. of Transport and Main Roads; Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

Yes

Name of Conference

ICONIP (Conference)