Applying Enhanced Real-Time Monitoring and Counting Method for Effective Traffic Management in Tashkent

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-05, Vol.23 (11), p.5007
• Main Authors: Kutlimuratov, Alpamis, Khamzaev, Jamshid, Kuchkorov, Temur, Anwar, Muhammad Shahid, Choi, Ahyoung
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Cameras; Closed circuit television; Computing time; Datasets; Deep learning; Efficiency; intelligent transportation system; Intelligent transportation systems; Kalman filters; Machine learning; Management decisions; Methods; Monitoring systems; Quality of life; Real time; Researchers; smart city; Surveillance; Traffic congestion; Traffic flow; Traffic management; Urban areas; vehicle counting; Vehicle identification; Vehicles; YOLOv5
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23115007

This study describes an applied and enhanced real-time vehicle-counting system that is an integral part of intelligent transportation systems. The primary objective of this study was to develop an accurate and reliable real-time system for vehicle counting to mitigate traffic congestion in a designated area. The proposed system can identify and track objects inside the region of interest and count detected vehicles. To enhance the accuracy of the system, we used the You Only Look Once version 5 (YOLOv5) model for vehicle identification owing to its high performance and short computing time. Vehicle tracking and the number of vehicles acquired used the DeepSort algorithm with the Kalman filter and Mahalanobis distance as the main components of the algorithm and the proposed simulated loop technique, respectively. Empirical results were obtained using video images taken from a closed-circuit television (CCTV) camera on Tashkent roads and show that the counting system can produce 98.1% accuracy in 0.2408 s.