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Demonstrates accurate vehicle detection and counting in urban environments, suggesting enhanced traffic monitoring solutions.
Accurate vehicle detection and counting play a crucial role in traffic monitoring, intelligent transportation systems, and urban planning. This study presents an algorithm for vehicle detection and counting using OpenCV, leveraging image processing and computer vision techniques. The proposed method incorporates background subtraction, contour detection, and object tracking to accurately identify and count vehicles in real-time video streams. The algorithm is tested on various traffic scenarios to evaluate its efficiency and accuracy. Experimental results demonstrate the effectiveness of the approach. The developed system provides a cost-effective and scalable solution for automated traffic analysis, offering potential applications in traffic management, congestion monitoring, and smart city initiatives.
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Babatunde et al. (2026) studied this question.
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