Non-motor vehicle illegal behavior discrimination and license plate detection based on real-time video

Abstract

In recent years, automatic detection technology of motor vehicles has developed rapidly. However, due to some interference problems, there is little research on the automatic detection technology of non-motor vehicle. This paper proposes a method which based on the experience and results of actual projects for discriminating non-motor vehicles in real-time video, detecting and recognizing license plates. The algorithm and steps will be described in detail. The image difference method and fractional differential method are combined to extract the contours of dynamic non-motor vehicles in the video, and the methods are used to filter the contours of non-motor vehicles using skin color filter detection and geometric feature discrimination to assist the training of cascade neural networks. Non-motor vehicle license plate image clustering is detected in the extracted contour by clustering the background color of the license plate and determining the ratio of the rectangle surrounding the license plate. The boundary expansion method is used in combination with the Faster R-CNN (Faster-Convolutional Neural Networks) to train the model, and then BP (Back propagation) neural network is used to identify characters in the target area. And a trajectory tracking method is proposed to automatically determine whether non-motorized vehicles have violations.