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

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Summary
This summary is machine-generated.

This study presents an enhanced real-time vehicle-counting system using YOLOv5 and DeepSort for intelligent transportation systems. The system achieves 98.1% accuracy in identifying and tracking vehicles to reduce traffic congestion.

Keywords:
YOLOv5intelligent transportation systemsmart cityvehicle counting

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Area of Science:

  • Intelligent Transportation Systems (ITS)
  • Computer Vision
  • Traffic Management

Background:

  • Traffic congestion is a major challenge in urban areas, impacting efficiency and quality of life.
  • Accurate real-time vehicle data is crucial for effective traffic management and the development of intelligent transportation systems.

Purpose of the Study:

  • To develop and enhance a real-time vehicle-counting system for intelligent transportation systems.
  • To improve the accuracy and reliability of vehicle identification and tracking.
  • To mitigate traffic congestion through precise real-time traffic monitoring.

Main Methods:

  • Vehicle identification using the You Only Look Once version 5 (YOLOv5) model for high performance and speed.
  • Vehicle tracking and counting employing the DeepSort algorithm, incorporating Kalman filter and Mahalanobis distance.
  • Utilizing a proposed simulated loop technique for accurate vehicle acquisition.
  • Empirical validation using video data from CCTV cameras on Tashkent roads.

Main Results:

  • The developed system demonstrates high accuracy in identifying and tracking vehicles.
  • The system achieved 98.1% accuracy in vehicle counting.
  • The processing time for the system was recorded at 0.2408 seconds, indicating efficient real-time performance.

Conclusions:

  • The enhanced real-time vehicle-counting system provides an accurate and efficient solution for traffic monitoring.
  • The integration of YOLOv5 and DeepSort algorithms significantly improves vehicle identification and tracking capabilities.
  • The system shows great potential for application in intelligent transportation systems to alleviate traffic congestion.