A pedestrian and vehicle rapid identification model based on convolutional neural network

Abstract

Image recognition technology based on convolutional neural network (CNN) has been widely used in the field of intelligent transportation in recent years. Since the image recognition in the field of intelligent transportation needs high real-time performance, this requires improving the speed of CNN. We refer to Overfeat, which was proposed in the ImageNet Large Scale Visual Recognition Challenge, to build a vehicle and pedestrian recognition model. We do not use the traditional sliding window method. Instead, we apply each convolution over the extent of the full image, eventually producing a map of output class predictions. This method ensures the accuracy of image recognition, while enhancing the operational efficiency and the real-time performance of CNN. In this paper, we use both a new method and the traditional sliding window method for the recognition of pedestrians and cars on the road. Then, we compare the advantages and disadvantages of the two methods in terms of their recognition effect and speed.