Urban Vehicle Tracking Using a Combined 3D Model Detector and Classifier

Authors:
Norbert Buch;Fei Yin;James Orwell;Dimitrios Makris;Sergio A. Velastin
Affiliations:
Digital Imaging Research Centre, Kingston University, Kingston upon Thames, United Kingdom KT1 2EE;Digital Imaging Research Centre, Kingston University, Kingston upon Thames, United Kingdom KT1 2EE;Digital Imaging Research Centre, Kingston University, Kingston upon Thames, United Kingdom KT1 2EE;Digital Imaging Research Centre, Kingston University, Kingston upon Thames, United Kingdom KT1 2EE;Digital Imaging Research Centre, Kingston University, Kingston upon Thames, United Kingdom KT1 2EE
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Year:
2009

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Abstract

This paper presents a tracking system for vehicles in urban traffic scenes. The task of automatic video analysis for existing CCTV infrastructure is of increasing interest due to benefits of behaviour analysis for traffic control. Based on 3D wire frame models, we use a combined detector and classifier to locate ground plane positions of vehicles. The proposed system uses a Kalman filter with variable sample time to track vehicles on the ground plane. The classification results are used in the data association of the tracker to improve consistency and for noise suppression. Quantitative and qualitative evaluation is provided using videos of the public benchmarking i-LIDS data set provided by the UK Home Office. Correctly detected tracks of 94% outperform a baseline motion tracker tested under the same conditions.