Real-time vehicle tracking system demonstrates improved accuracy and speed in urban traffic monitoring, suggesting better management of roadways.
The rapid growth of urbanization has resulted in increased vehicle density on roads, raising the demand for efficient and intelligent traffic monitoring systems. This paper presents a real-time vehicle detection, tracking, and recognition system using YOLOv26(ultralytics), the latest advancement in the You Only Look Once (YOLO) architecture. The proposed system leverages deep learning-based object detection to detect and classify vehicles from video streams captured by surveillance cameras. YOLOv26(ultralytics) offers improved accuracy and speed over its predecessors, making it highly suitable for real-time Intelligent Transportation System (ITS) applications. The system incorporates Deep SORT for robust multi-object tracking and supports recognition based on vehicle attributes including color, type, and license plate.
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K et al. (2026) studied this question.
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