REAL-TIME VEHICLE DETECTION AND TRACKING USING DEEP NEURAL NETWORKS

被引：0

作者：

Gu, Xiao-Feng ^{[1
]}

Chen, Zi-Wei ^{[1
]}

Ma, Ting-Song ^{[1
]}

Li, Fan ^{[1
]}

Yan, Long ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Int Ctr Wavelet Anal & Its Applicat, Chengdu 611731, Sichuan, Peoples R China

[2] State Grid Inner Shizuishan Power Supply Co, Shizuishan 753000, Peoples R China

来源：

2016 13TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP) | 2016年

基金：

国家高技术研究发展计划(863计划); 中国国家自然科学基金;

关键词：

Vehicle detection; Vehicle tracking; OAUE; Deep neural networks;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：167 / 170

页数：4

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