Intelligent ROI-Based Vehicle Counting Framework for Automated Traffic Monitoring
For traffic monitoring systems, this framework provides a computationally efficient and highly accurate solution for real-time vehicle counting, outperforming existing techniques in complex multi-road scenarios.
The paper proposes an automated vehicle counting framework that optimizes both accuracy and efficiency by automatically determining the optimal region of interest (ROI) using a combination of detection scores, tracking scores, and vehicle density. The framework achieves up to 100% accuracy on benchmark datasets and is up to four times faster than full-frame processing.
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. Our framework operates in two distinct phases: \textit{estimation} and \textit{prediction}. 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, and vehicle density. This adaptive approach ensures compatibility with any detection and tracking method, enhancing the framework's versatility. In the prediction phase, vehicle counting is efficiently performed within the estimated ROI. We evaluated our framework on benchmark datasets like UA-DETRAC, GRAM, CDnet 2014, and ATON. Results demonstrate exceptional accuracy, with most videos achieving 100\% accuracy, while also enhancing computational efficiency, making processing up to four times faster than full-frame processing. The framework outperforms existing techniques, especially in complex multi-road scenarios, demonstrating robustness and superior accuracy. These advancements make it a promising solution for real-time traffic monitoring.