Real-time vehicle detection and tracking in video based on faster R-CNN

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Zhang, Yongjie^1 ; Wang, Jian^1 ; Yang, Xin^2

Department of Electronic Engineering, Tsinghua University, Beijing

100084, China^1

Institute of Information Optics Engineering, Soochow University, Suzhou, JIANGSU

210056, China^2

关键词: Complex background;    Detection accuracy;    Detection methods;    Image information;    Parallel Computation;    Real-time application;    Real-time detection;    Vehicle detection;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012068/pdf
DOI  :  10.1088/1742-6596/887/1/012068

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.