[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]

Copy

Design of a road vehicle detection systembased on monocular vision()

Share：

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

Author(s):

Wang Hai;  Zhang Weigong;  Cai Yingfeng

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

vehicle detection;  monocular vision;  edge and symmetry fusion;  Gabor feature;  PNN network

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.

References:

[1] Kim S, Oh S Y, Kang J, et al. Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion[C]//2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. Alberta, Canada, 2005: 2173-2178.
[2] Giancarlo A, Alberto B, Pietro C. Vehicle guard rail detection using radar and vision data fusion[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(1): 95-105.
[3] Fang J, Meng H, Zhang H, et al. A low-cost vehicle detection and classification system based on unmodulated continuous-wave radar[C]//IEEE International Conference on Intelligent Transportation Systems Conference. Seattle, USA, 2007: `715-720.
[4] Acunzo D, Zhu Y, Xie B, et al. Context-adaptive approach for vehicle detection under varying lighting conditions[C]//IEEE International Conference on Intelligent Transportation Systems Conference. Seattle, USA, 2007: 654-660.
[5] Mahlisch M, Schweiger R, Ritter W, et al. Sensor fusion using spatio-temporal aligned video and lidar for improved vehicle detection[C]//IEEE Intelligent Vehicles Symposium. Tokyo, Japan, 2006: 424-429.
[6] Sun Z, Bebis G, Miller R. Monocular precrash vehicle detection: features and classifiers[J]. IEEE Transactions on Image Processing, 2006, 15(7): 2019-2034.
[7] Iwasaki Y, Kurogi Y. Real-time robust vehicle detection through the same algorithm both day and night[C]//International Conference on Wavelet Analysis and Pattern Recognition. Beijing, China, 2007: 1008-1014.
[8] Cheng H, Zheng N, Sun C. Boosted Gabor features Applied to vehicle detection[C]//18th International Conference on Pattern Recognition. Hong Kong, China, 2006: 662-666.
[9] Lan J, Zhang M. A new vehicle detection algorithm for real-time image processing system[C]//2010 International Conference on Computer Application and System Modeling. Taiyuan, China, 2010: 1-4.
[10] Luo W T, Jun W H, Kuo C F. Vehicle detection using normalized color and edge map[J]. IEEE Transactions on Image Processing, 2007, 16(3): 850-864.
[11] Wen X, Zhao H, Wang N, et al. A rear-vehicle detection system for static images based on monocular vision[C]//9th International Conference on Control, Automation, Robotics and Vision. Singapore, 2006: 1-4.
[12] Schneiderman H, Kanade T. A statistical method for 3D object detection applied to faces and cars[C]//IEEE International Conference on Computer Vision and Pattern Recognition. Hilton Head Island, SC, USA, 2000: 746-751.
[13] Sun Z, Bebis G, Miller R. On-road vehicle detection using gabor filters and support vector machines[C]//14th International Conference on Digital Signal Processing. Reno, USA, 2002: 1019-1022.

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]

Common functions