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Computer vision techniques for traffic flow computation

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Abstract

This paper describes an application of computer vision techniques to road surveillance. It reports on a project undertaken in collaboration with the Research and Innovation group at the Ordnance Survey. The project aims to produce a system that detects and tracks vehicles in real traffic scenes to generate meaningful parameters for use in traffic management. The system has now been implemented using two different approaches: a feature-based approach that detects and groups corner features in a scene into potential vehicle objects, and an appearance-based approach that trains a cascade of classifiers to learn the appearances of vehicles as an arrangement of a set of pre-defined simple Haar features. Potential vehicles detected are then tracked through an image sequence, using the Kalman filter motion tracker. Experimental results of the algorithms are presented in this paper.

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Acknowledgements

Yan Wang was supported by Ordnance Survey under its Postgraduate Research Program.

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Authors and Affiliations

  1. School of Computer Science and Information Technology, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK

    Li Bai & Yan Wang

  2. Ordnance Survey, Research and Innovation, Romsey Road, Southampton, SO16 4GU, UK

    William Tompkinson

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  1. Li Bai
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  2. William Tompkinson
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  3. Yan Wang
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Correspondence to Li Bai.

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Bai, L., Tompkinson, W. & Wang, Y. Computer vision techniques for traffic flow computation. Pattern Anal Applic 7, 365–372 (2004). https://doi.org/10.1007/s10044-004-0238-x

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  • DOI: https://doi.org/10.1007/s10044-004-0238-x

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