Incorporating Vehicle Detection Algorithms via Edge Computing on a Campus Digital Twin Model-Reference-Cited by-同舟云学术

Incorporating Vehicle Detection Algorithms via Edge Computing on a Campus Digital Twin Model

Published:2023-06-13 Issue: Volume: Page:
ISSN:
Container-title:International Conference on Transportation and Development 2023
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Author:

Azfar Talha¹²²²²,Wang Chengyue¹²²²²,Ke Ruimin¹²²²²,Raheem Adeeba¹²²²²,Weidner Jeffrey¹²²²²,Cheu Ruey L.¹²²²²

Affiliation:

1. Dept. of Electrical and Computer Engineering, Univ. of Texas at El Paso, El Paso, TX.

2. Dept. of Civil Engineering, Univ. of Texas at El Paso, El Paso, TX.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784484876.035

Reference27 articles.

1. ATLDOT (Atlanta Department of Transportation). “North Ave Smart Corridor.” https://atldot. atlantaga.gov/projects/north-ave-smart-corridor. Accessed: Nov 25 2022.

2. Azfar T. Li J. Yu H. Cheu R. L. Lv Y. and Ke R. (2022a). “Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review”. arXiv preprint arXiv:2211.05120.

3. Azfar, T., Weidner, J., Raheem, A., Ke, R., and Cheu, R. L. (2022b). “Efficient Procedure of Building University Campus Models for Digital Twin Simulation,” In IEEE Journal of Radio Frequency Identification, doi: 10.1109/JRFID.2022.3212957.

4. A Review of Computer Vision Techniques for the Analysis of Urban Traffic,;Buch N.;IEEE Transactions on Intelligent Transportation Systems,2011

5. MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms

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