Multi-View Vehicle Detection and Tracking for Smart City Traffic Monitoring
DOI:
https://doi.org/10.4108/eetiot.12412Keywords:
Deep Learning, Intelligent transportation systems, YOLOv11, Vehicle Detection, Multi-Object Tracking, SAHIAbstract
Urban traffic monitoring plays a crucial role in intelligent transportation systems. The development of surveillance camera networks has generated a large amount of image and video data that can be exploited for traffic flow detection, tracking, and analysis tasks. However, detecting and tracking vehicles from fixed traffic surveillance cameras still faces many challenges. The main challenges include obscured objects, small target size, and high traffic density. This study presents a deep learning-based traffic monitoring framework for detecting and tracking multiple objects in urban traffic monitoring systems. The proposed framework integrates YOLOv11 for vehicle detection and DeepSORT with a Kalman filter-based state estimation method for tracking multiple objects. In addition, the SAHI technique is integrated to investigate its ability to support the detection of small objects in traffic data. The research framework was evaluated using a dataset collected from traffic cameras in Thai Nguyen, Vietnam. Numerous test scenarios were conducted with varying traffic densities, observation distances, and camera viewing angles. Experimental results showed that the YOLOv11 configuration combined with DeepSORT achieved a processing speed of approximately 10.1 FPS; for object detection tasks, the model achieved an mAP@0.5 of 0.66. Simultaneously, experimental results show that the proposed framework can maintain vehicle detection and tracking across consecutive frames under varying observation conditions. In addition, the integration of SAHI techniques recorded an improvement in detecting small objects, with mAP@0.5 increasing from 0.66 to 0.70 and AP_S increasing from 0.29 to 0.40. The results obtained demonstrate the potential applicability of the proposed framework to traffic detection, tracking, and monitoring problems in urban environments.
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Copyright (c) 2026 Thang C. Vu, Dung T. Nguyen, Long Q. Dinh, Mui D. Nguyen, Minh T. Nguyen
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