[1] Wang Hai, Zhang Weigong, Cai Yingfeng,. Design of a road vehicle detection systembased on monocular vision [J]. Journal of Southeast University (English Edition), 2011, 27 (2): 169-173. [doi:10.3969/j.issn.1003-7985.2011.02.011]
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Design of a road vehicle detection systembased on monocular vision(
)
基于单目视觉的道路车辆检测系统设计
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 27
- Issue:
- 2011 2
- Page:
- 169-173
- Research Field:
- Computer Science and Engineering
- Publishing date:
- 2011-06-30
Info
- Title:
- Design of a road vehicle detection systembased on monocular vision
- 基于单目视觉的道路车辆检测系统设计
- Author(s):
- Wang Hai; Zhang Weigong; Cai Yingfeng
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
- PACS:
- TP391.4
- DOI:
- 10.3969/j.issn.1003-7985.2011.02.011
- Abstract:
- In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor(DSP)micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is used and a low threshold is set so that all the possible vehicles have a nearly 100% detection rate(TP)and the non-vehicles have a high false detection rate(FP), i.e., all the possible vehicles can be obtained. In the second step, a classifier using a probabilistic neural network(PNN)which is based on multiple scales and an orientation Gabor feature is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that the proposed system maintains a high detection rate and a low false detection rate under different road, weather and lighting conditions.
- 为了减少车辆碰撞, 设计了一个新的基于单目视觉的道路前方车辆检测系统.首先, 建立一个基于DM642数字信号处理器的小型灵活硬件平台.然后, 提出了一个2步车辆检测算法.第1步, 采用车辆边缘和对称性融合算法, 设定较低阈值检测出所有可能车辆, 该步骤对车辆有接近100%的检测率(TP)和对非车辆有较高的误检率(FP), 即能获得图像中所有可能车辆;第2步, 采用多尺度多方向Gabor特征对车辆样本进行表征, 并用概率神经网络对大量车辆和非车辆样本进行训练, 从第1步获得的可能车辆中识别出正确的车辆.实验表明:所设计的系统在不同道路、天气和光照条件下都具有很高的检测率和较低的误检率.
References:
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Memo
- Memo:
-
Biographies: Wang Hai(1983—), male, graduate; Zhang Weigong(corresponding author), male, doctor, professor, zhangwg@seu.edu.cn.
Foundation items: The National Key Technology R& D Program of China during the 11th Five-Year Plan Period(2009BAG13A04), Jiangsu Transportation Science Research Program(No.08X09), Program of Suzhou Science and Technology(No.SG201076).
Citation: Wang Hai, Zhang Weigong, Cai Yingfeng.Design of a road vehicle detection system based on monocular vision[J].Journal of Southeast University(English Edition), 2011, 27(2):169-173.[doi:10.3969/j.issn.1003-7985.2011.02.011]