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Robust Vehicle Detection and Counting Algorithm Adapted to Complex Traffic Environments with Sudden Illumination

Yue Chen¹, Wusheng Hu¹

¹School of Transportation, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)

|May 14, 2020

Summary

This study introduces a new method for real-time vehicle detection and counting, achieving 99.93% accuracy in challenging traffic conditions. The approach effectively handles illumination changes and complex urban environments for improved traffic management.

Keywords:

Normative-Lane and Non-Normative-Lane real-time background vehicle counting vehicle detection

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Area of Science:

Computer Vision
Traffic Engineering
Artificial Intelligence

Background:

Real-time vehicle detection and counting are vital for traffic control.
Traditional methods struggle with environmental challenges like illumination changes, shadows, and complex urban traffic.
Video-based traffic information offers significant advantages over conventional technologies.

Purpose of the Study:

To develop a robust vehicle detection and counting method adaptable to adverse environmental conditions.
To improve the accuracy and reliability of traffic monitoring systems.
To address limitations of current algorithms in handling sudden illumination shifts, vehicle shadows, and non-normative driving.

Main Methods:

A real-time background model was employed to manage sudden illumination changes.
A motion-based detection method was utilized for vehicle shadow removal.
Vehicle counting was implemented using two Region of Interest (ROI) types: Normative-Lane and Non-Normative-Lane, to accommodate complex urban traffic and non-normative driving behaviors.

Main Results:

The proposed method achieved a vehicle counting accuracy of 99.93% under adverse environmental conditions.
The Normative-Lane and Non-Normative-Lane system effectively detected non-normative driving behaviors.
The methodology demonstrated significant improvements in counting accuracy within complex urban traffic scenarios.

Conclusions:

The developed method provides a highly accurate and robust solution for real-time vehicle detection and counting.
The system's ability to handle challenging environments and non-normative driving enhances traffic management capabilities.
This research contributes to advancing intelligent transportation systems through improved video-based traffic analysis.