This paper presents a new real-time intelligent traffic monitoring system. To perform the vehicle detection, a filtered You Only Look Once (YOLO) is used. The pre-trained YOLO framework can detect 80 objects. The proposed system is tested for three classes of vehicles such as bus, truck, and car. After extracting the three categories, to obtain the count of that vehicle in each lane, checkpoint is assigned. The count is used to control the real-time road traffic signal. The system is tested with three different publicly available traffic videos. In the present work, we have used Kernel Correlation Filter (KCF) tracker and the object retrieval accuracy is obtained. Experimental results show that YOLO and KCF outperform Scale Invariant Feature Transform (SIFT) and Region-based Convolutional Neural Network (RCNN) with KCF tracker, and Maximally Stable Extremal Regions (MSER) and faster RCNN with KCF tracker.
Object Detection Using YOLO Framework for Intelligent Traffic Monitoring
Lect. Notes Electrical Eng.
Machine Vision and Augmented Intelligence—Theory and Applications ; Kapitel : 34 ; 405-412
11.11.2021
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Intelligent traffic monitoring , Vehicle detection , Vehicle tracking , Vehicle counting Computer Science , User Interfaces and Human Computer Interaction , Image Processing and Computer Vision , Computer Communication Networks , Health Informatics , Artificial Intelligence , Communications Engineering, Networks
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