Vehicle counting plays an important role in traffic management and surveillance systems. Counting the vehicles is challenging due to various factors, such as lighting variations, occlusions, and diverse vehicle types. Existing methods for vehicle counting often relied on manual or semi-automated methods, which are both labor intensive and are also, bound to have some human error. Automating the counting process using deep learning algorithms can provide a more efficient and accurate solution. This work aims to detect and count vehicles in real-time video streams for which real-time recorded video datasets are used. A centroid-based tracking algorithm has been implemented to track the vehicles across consecutive frames, the algorithm associates the center points of vehicles over time, thereby enabling the tracking of individual vehicles as they move throughout the video. The evaluation demonstrates YOLOv8’s high accuracy in detecting and counting vehicles in comparison with YOLOv5, which makes it suitable for diverse real-world applications such as traffic-flow analysis, congestion management, and the system’s real-time processing capabilities which further enhance its practical utility in surveillance systems.
Real-Time Vehicle Detection and Counting for Traffic Management System
Lect. Notes in Networks, Syst.
International Conference on Data Science and Applications ; 2024 ; Jaipur, India July 17, 2024 - July 19, 2024
2025-06-07
12 pages
Article/Chapter (Book)
Electronic Resource
English
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