Motion-constrained Road User Tracking for Real-time Traffic Analysis

Nyan Bo Bo, Nyan Bo Bo, Peter Veelaert, Peter Veelaert, Wilfried Philips, Wilfried Philips

2022

Abstract

Reliability of numerous smart traffic applications are highly dependent on the accuracy of underlying road user tracker. Demand on scalability and privacy preservation pushes vision-based smart traffic applications to sense and process images on edge devices and transmit only concise information to decision/fusion nodes. One of the requirements for deploying a vision algorithm on edge devices is its ability to process captured images in real time. To meet these needs, we propose a real-time road user tracker which outperforms state-of-the-art trackers. Our approach utilizes double thresholding on detector responses to suppress initialization of false positive trajectories while assuring corresponding detector responses required for updating trajectories are not wrongly discarded. Furthermore, our proposed Bayes filter reduces fragmentation and merging of trajectories which highly effect the performance of subsequent smart traffic applications. The performance of our tracker is evaluated on the real life traffic data in turning movement counting (TMC) application and it achieves a high precision of 96% and recall of 95% while state-of-the-art tracker in comparison achieves 92% on precision and 87% on recall.

Paper Citation

in Harvard Style

Bo N., Veelaert P. and Philips W. (2022). Motion-constrained Road User Tracking for Real-time Traffic Analysis. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 785-792. DOI: 10.5220/0010788700003124

in Bibtex Style

@conference{visapp22,
author={Nyan Bo Bo and Peter Veelaert and Wilfried Philips},
title={Motion-constrained Road User Tracking for Real-time Traffic Analysis},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={785-792},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010788700003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Motion-constrained Road User Tracking for Real-time Traffic Analysis
SN - 978-989-758-555-5
AU - Bo N.
AU - Veelaert P.
AU - Philips W.
PY - 2022
SP - 785
EP - 792
DO - 10.5220/0010788700003124
PB - SciTePress