An Integrated Approach for Vehicle Detection and Type Recognition

Abstract

Vehicle detection and type recognition are important for intelligent transportation systems in smart cities. The real time high accuracy recognition with affordable hardware is a challenging issue due to the complexities of video data. In this paper, we propose an integrated approach that combining traditional three-frame difference and deep Convolutional Neural Networks (DCNNs) to detect vehicle and recognize vehicle type in traffic videos captured with fixed mounted cameras. This integrated approach can take advantage of the real-time motion detection ability of three-frame difference and capabilities of image recognition of DCNNs. We have evaluated the proposed approach using road traffic videos in terms of accuracy and performance, which show very promising results.