Intelligent ROI-Based Vehicle Counting Framework for Automated Traffic Monitoring
Summary: arXiv:2604.12470v1 Announce Type: new
Abstract
Accurate vehicle counting through video surveillance is crucial for efficient traffic management. However, achieving high counting accuracy while ensuring computational efficiency remains a challenge. To address this, we propose a fully automated, video-based vehicle counting framework designed to optimize both computational efficiency and counting accuracy.
Framework Overview
Our framework operates in two distinct phases: estimation and prediction.
Estimation Phase
In the estimation phase, the optimal region of interest (ROI) is automatically determined using a novel combination of three models based on:
- Detection scores
- Tracking scores
- Vehicle density
This adaptive approach ensures compatibility with any detection and tracking method, enhancing the framework’s versatility and overall performance.
Prediction Phase
In the prediction phase, vehicle counting is efficiently performed within the estimated ROI. This structured approach allows for precise counting while maintaining high computational efficiency.
Performance Evaluation
We evaluated our framework on benchmark datasets including:
- UA-DETRAC
- GRAM
- CDnet 2014
- ATON
Results from these evaluations demonstrate exceptional accuracy, with most videos achieving 100% accuracy. Additionally, our framework enhances computational efficiency, making processing up to four times faster than traditional full-frame processing methods.
Comparative Analysis
When compared to existing techniques, our framework shows significant advantages, particularly in challenging multi-road scenarios. Its robustness and superior accuracy make it an ideal solution for real-time traffic monitoring applications.
Conclusion
In conclusion, the Intelligent ROI-Based Vehicle Counting Framework represents a significant advancement in automated traffic monitoring. By merging optimal ROI estimation with efficient vehicle counting, this framework not only meets but exceeds the current demands for accuracy and computational efficiency in traffic management systems. Its versatility and performance make it a promising tool for future developments in smart city planning and infrastructure management.