Abstract
Vehicle detection from traffic monitoring video, which lays the foundation to serials consequential operations such as vehicle counting and accident detection, is an essential part of traffic monitoring system. Traditional target detection methods always have some kind of drawbacks more or less, while target detection based on deep learning has the benefits of more abundant target feature extraction, thus much higher detection accuracy can be achieved, of which YOLO v3 is a typical representative. In this paper, the model structure and principles of YOLOv3 is analyzed in depth firstly, and then its application to vehicle detection in road surveillance video is carried out with the discussion of some existing problems.
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