Real-Time vehicle detection and tracking using deep neural networks

Abstract

Dynamic vehicle detection and tracking can provide essential data to solve the problem of road planning and traffic management. A method for real-time vehicle detection and tracking using deep neural networks is proposed in this paper and a complete network architecture is presented. Using our model, you can obtain vehicle candidates, vehicle probabilities, and their coordinates in real-time. The proposed model is trained on the PASCAL VOC 2007 and 2012 image set and tested on ImageNet dataset. By a carefully design, the detection speed of our model is fast enough to process streaming video. Experimental results show that proposed model is a real-time, accurate vehicle detector, making it ideal for computer vision application.