Abstract
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.
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