Detection and Counting of Traffic in Two-Way and Four-Way Traffic Modes
Keywords:
Traffic Control Systems, Pre-trained Machine Learning Models, Bounding Boxes, Precise Enumeration, Vital Perspectives, Enormous Potential of Computer
Abstract
Vehicle counting and detection using OpenCV is a vital computer vision application, enhancing surveillance and traffic control systems. By leveraging OpenCV’s capabilities, developers create systems to detect and track vehicles, including two-wheelers, using strategically placed cameras. Deep learning models like YOLO or pre-trained models such as Haar Cascade Classifiers identify vehicles within video frames, assigning bounding boxes to track movement across frames. This enables accurate counting as vehicles enter or exit the monitored area, with applications in toll collection, parking management, traffic flow analysis, and security. By precisely recognizing and tracking vehicles, these systems provide critical insights for traffic management, helping to reduce congestion, prevent accidents, and optimize parking usage. Additionally, they enable automatic vehicle tracking for security purposes, enhancing monitoring and safety. In summary, OpenCV’s application in vehicle detection and counting underscores the vast potential of computer vision technology in various industries.
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References
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Published
2024-10-29
How to Cite
Regin, R., & Rajest, S. (2024). Detection and Counting of Traffic in Two-Way and Four-Way Traffic Modes. Central Asian Journal of Theoretical and Applied Science, 5(6), 579-591. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/1512
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Articles
