2nd Annual International Conference on Information System and Artificial Intelligence | |
Real-time vehicle detection and tracking in video based on faster R-CNN | |
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 |
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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.
【 预 览 】
Files | Size | Format | View |
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Real-time vehicle detection and tracking in video based on faster R-CNN | 474KB | download |