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A computer vision system for the detection and classification of vehicles at urban road intersections

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Abstract

The paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences coming from pole-mounted video cameras at urban crossroads. The system uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing a robust background updating, and a feature-based tracking method. It is able to describe the path of each detected vehicle, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one for distinguishing between classes having similar models, like bicycles and motorcycles. The system is flexible with respect to the intersection geometry and the camera position. Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a 100,000-people town in northern Italy.

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Acknowledgements

This work was partially supported by Comune di Trento and Italian Ministero delle Infrastrutture e Trasporti. We thank Luca Leonelli, Servizio Reti of Comune di Trento, for testing the system, reporting bugs, and suggesting improvements.

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Authors and Affiliations

  1. ITC-irst, Via Sommarive 18, 38050, Povo, Trento, Italy

    Stefano Messelodi, Carla Maria Modena & Michele Zanin

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  1. Stefano Messelodi
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  2. Carla Maria Modena
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  3. Michele Zanin
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Correspondence to Stefano Messelodi.

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Messelodi, S., Modena, C.M. & Zanin, M. A computer vision system for the detection and classification of vehicles at urban road intersections. Pattern Anal Applic 8, 17–31 (2005). https://doi.org/10.1007/s10044-004-0239-9

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  • DOI: https://doi.org/10.1007/s10044-004-0239-9

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