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Distributed multi-class road user tracking in multi-camera network for smart traffic applications

Bo Bo Nyan (UGent) , Maarten Slembrouck (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)
Author
Organization
Abstract
Reliable tracking of road users is one of the important tasks in smart traffic applications. In these applications, a network of cameras is often used to extend the coverage. However, efficient usage of information from cameras which observe the same road user from different view points is seldom explored. In this paper, we present a distributed multi-camera tracker which efficiently uses information from all cameras with overlapping views to accurately track various classes of road users. Our method is designed for deployment on smart camera networks so that most computer vision tasks are executed locally on smart cameras and only concise high-level information is sent to a fusion node for global joint tracking. We evaluate the performance of our tracker on a challenging real-world traffic dataset in an aspect of Turn Movement Count (TMC) application and achieves high accuracy of 93%and 83% on vehicles and cyclist respectively. Moreover, performance testing in anomaly detection shows that the proposed method provides reliable detection of abnormal vehicle and pedestrian trajectories.
Keywords
road user tracking, smart camera network, distributed computing, trajectory analysis, road traffic statistics, smart traffic

Citation

Please use this url to cite or link to this publication:

MLA
Nyan, Bo Bo, et al. “Distributed Multi-Class Road User Tracking in Multi-Camera Network for Smart Traffic Applications.” ACIVS 2020, Advanced Concepts for Intelligent Vision Systems, 2020.
APA
Nyan, B. B., Slembrouck, M., Veelaert, P., & Philips, W. (2020). Distributed multi-class road user tracking in multi-camera network for smart traffic applications. In ACIVS 2020, Advanced Concepts for Intelligent Vision Systems. Auckland, New Zealand.
Chicago author-date
Nyan, Bo Bo, Maarten Slembrouck, Peter Veelaert, and Wilfried Philips. 2020. “Distributed Multi-Class Road User Tracking in Multi-Camera Network for Smart Traffic Applications.” In ACIVS 2020, Advanced Concepts for Intelligent Vision Systems.
Chicago author-date (all authors)
Nyan, Bo Bo, Maarten Slembrouck, Peter Veelaert, and Wilfried Philips. 2020. “Distributed Multi-Class Road User Tracking in Multi-Camera Network for Smart Traffic Applications.” In ACIVS 2020, Advanced Concepts for Intelligent Vision Systems.
Vancouver
1.
Nyan BB, Slembrouck M, Veelaert P, Philips W. Distributed multi-class road user tracking in multi-camera network for smart traffic applications. In: ACIVS 2020, Advanced Concepts for Intelligent Vision Systems. 2020.
IEEE
[1]
B. B. Nyan, M. Slembrouck, P. Veelaert, and W. Philips, “Distributed multi-class road user tracking in multi-camera network for smart traffic applications,” in ACIVS 2020, Advanced Concepts for Intelligent Vision Systems, Auckland, New Zealand, 2020.
@inproceedings{8635084,
  abstract     = {Reliable tracking of road users is one of the important tasks in smart traffic applications. In these applications, a network of cameras is often used to extend the coverage. However, efficient usage of information from cameras which observe the same road user from different view points is seldom explored. In this paper, we present a distributed multi-camera tracker which efficiently uses information from all cameras with overlapping views to accurately track various classes of road users. Our method is designed for deployment on smart camera networks so that most computer vision tasks are executed locally on smart cameras and only concise high-level information is sent to a fusion node for global joint tracking. We evaluate the performance of our tracker on a challenging real-world traffic dataset in an aspect of Turn Movement Count (TMC) application and achieves high accuracy of 93%and 83% on vehicles and cyclist respectively. Moreover, performance testing in anomaly detection shows that the proposed method provides reliable detection of abnormal vehicle and pedestrian trajectories.},
  author       = {Nyan, Bo Bo and Slembrouck, Maarten and Veelaert, Peter and Philips, Wilfried},
  booktitle    = {ACIVS 2020, Advanced Concepts for Intelligent Vision Systems},
  keywords     = {road user tracking,smart camera network,distributed computing,trajectory analysis,road traffic statistics,smart traffic},
  language     = {eng},
  location     = {Auckland, New Zealand},
  title        = {Distributed multi-class road user tracking in multi-camera network for smart traffic applications},
  year         = {2020},
}