Vehicle Detection and Localization for Autonomous Traffic Monitoring Systems in Unstructured Crowded Scenes

This chapter presents the application of YOLO, a deep learning-based object detection algorithm, for traffic monitoring. The algorithm was applied to real-time video streams from roadway cameras to detect and track vehicles. The results were compared with traditional computer vision methods and showed superior accuracy and processing speed. This study highlights the potential of YOLO for traffic monitoring and the significance of incorporating deep learning into intelligent transportation systems. YOLO V7 outperforms all other real-time object detectors on the GPU V100 in terms of speed and accuracy in the range of 5 to 160 frames per second and has the highest accuracy of 56.8% AP. YOLO V7 also introduces a new training methodology that improves the convergence rate and the generalization capabilities of the model. Experimental results show that YOLO V7 outperforms existing methods in terms of accuracy, speed, and efficiency, making it an attractive solution for real-world applications.

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Vehicle Detection and Localization for Autonomous Traffic Monitoring Systems in Unstructured Crowded Scenes

Cited by 3 publications

References 8 publications

Vehicle Tracking based on an Improved DeepSORT Algorithm and the YOLOv4 Framework

Vehicle Tracking based on an Improved DeepSORT Algorithm and the YOLOv4 Framework

A Novel Survey on ML based Vehicle Detection for Dynamic Traffic Control

Real-Time Object Detection in Video for Traffic Monitoring

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