TY - JOUR
T1 - Automatic Lane Discovery and Traffic Congestion Detection in a Real-Time Multi-Vehicle Tracking Systems
AU - Wang, Lu
AU - Law, K. L.Eddie
AU - Lam, Chan Tong
AU - Ng, Benjamin
AU - Ke, Wei
AU - Im, Marcus
N1 - Publisher Copyright:
© 2024 The Authors.
PY - 2024
Y1 - 2024
N2 - The Macao Government provides web-based streaming videos for the public to monitor live traffic and road conditions across the city. This allows individuals to review the latest road traffic conditions online before planning their travels. To let road user makes better and faster decisions, it is desirable to design an automated subsystem in an Intelligent Transportation System (ITS). And the subsystem should analyze available live video streams and recommend multiple travel routes, if possible, to each query instantly. In the paper, we propose a real-time road traffic condition evaluation system. Its design is based on a combination of deep learning models (YOLO and BoTSORT), and a modified Non-Maximum Suppression (mNMS) algorithm. The mNMS strategy removes the needs to manually tune the NMS parameters. By deploying YOLO with our mNMS, the object detection efficiency on live videos improves significantly. Together with the BoTSORT method, we can track the moving vehicles, create the corresponding motion trajectories, and identify traffic lanes with high correctness. The generated trajectory then operates as a filtering mechanism in assessing real-time road traffic conditions. By separating the lanes based on observation angles and using a per-lane status score independently, we further enhance the overall system performance. Through thorough experiments on the live videos, our design correctly estimates traffic status with high accuracy and without needing any manual parametric adjustments.
AB - The Macao Government provides web-based streaming videos for the public to monitor live traffic and road conditions across the city. This allows individuals to review the latest road traffic conditions online before planning their travels. To let road user makes better and faster decisions, it is desirable to design an automated subsystem in an Intelligent Transportation System (ITS). And the subsystem should analyze available live video streams and recommend multiple travel routes, if possible, to each query instantly. In the paper, we propose a real-time road traffic condition evaluation system. Its design is based on a combination of deep learning models (YOLO and BoTSORT), and a modified Non-Maximum Suppression (mNMS) algorithm. The mNMS strategy removes the needs to manually tune the NMS parameters. By deploying YOLO with our mNMS, the object detection efficiency on live videos improves significantly. Together with the BoTSORT method, we can track the moving vehicles, create the corresponding motion trajectories, and identify traffic lanes with high correctness. The generated trajectory then operates as a filtering mechanism in assessing real-time road traffic conditions. By separating the lanes based on observation angles and using a per-lane status score independently, we further enhance the overall system performance. Through thorough experiments on the live videos, our design correctly estimates traffic status with high accuracy and without needing any manual parametric adjustments.
KW - Deep learning algorithms
KW - intelligent transportation systems
KW - multi-vehicle tracking
KW - road traffic status monitoring
UR - http://www.scopus.com/inward/record.url?scp=85207802593&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3483439
DO - 10.1109/ACCESS.2024.3483439
M3 - Article
AN - SCOPUS:85207802593
SN - 2169-3536
VL - 12
SP - 161468
EP - 161479
JO - IEEE Access
JF - IEEE Access
ER -