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基于ARM11的单目视觉车距监测系统 被引量:4

Monocular Vision Car Distance Monitoring System Based on ARM11
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摘要 驾驶员行车时易出现视觉疲劳和失速现象,此时难以判断安全车间距,易导致车辆追尾事故.针对这一问题,以ARM11器件SAMSUNG S3C6410为核心搭建了安全车距监测系统;结合车辆的形态和纹理等特征,采用多特征融合技术完成复杂环境下的车辆识别,并根据单目视觉测距原理完成摄像机多角度下的车距检测.实验结果表明,该系统能有效监测前车车距,测量准确、可靠性高且满足实时性要求. Driver while driving prone to visual fatigue and stall phenomena, at this time it i~ difficult to judge the safety car distance, easily lead to rear-end collision accident. In order to solve this problem, uses the ARM11 core SAMSUNG S3C6410 devices build a car safety distance monitoring system. Combine of vehicle features such as shape and texture, system uses multi-feature fusion technology to complete the vehicle identification under complex environment, uses the monocular vision principle complete the car distance detection with the camera under multi-angle. The experiments show that this system can effective monitoring the front cars distance, accurate measurement, high reliability and meet the requirements of real-time.
出处 《计算机系统应用》 2012年第12期33-37,共5页 Computer Systems & Applications
基金 国家高技术研究发展计划(863)(2009AA11Z203)
关键词 安全车距监测 单目视觉 多特征融合 SAMSUNG S3C6410 safety car distance monitoring monocular vision multi-feature fusion SAMSUNG S3C6410
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  • 1谢云,杨宜民.全自主机器人足球系统的研究综述[J].机器人,2004,26(5):474-480. 被引量:21
  • 2Kilger M.A shadow handler in a video-based real-time traffic monitoring system[A].In:Proceedings of IEEE Workshop on Applications of Computer Vision[C],Palm Springs,CA,USA,1992:1060 ~ 1066.
  • 3Elgammal A.Background and foreground modeling using nonparametric kernel density estimation for visual surveillance[J].Proceedings of IEEE,2002,90 (7):1151 ~ 1163.
  • 4Friedman N,Russell S.Image segmentation in video sequences:A probabilistic approach[A].In:Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence[C],Rhode Island,USA,1997:175 ~ 181.
  • 5Grimson W,Stauffer C,Romano R.Using adaptive tracking to classify and monitor activities in a site[A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C],Santa Barbara,CA,USA,1998:22 ~29.
  • 6Stauffer C,Grimson W.Adaptive background mixture models for realtime tracking[A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C],Fort Collins,Colorado,USA,1999,2:246~252.
  • 7Gao X,Boult T,Coetzee F,et al.Error analysis of background adaption[A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[C],Hilton Head Isand,SC,USA,2000:503 ~510.
  • 8Power P W,Schoonees J A.Understanding background mixture models for foreground segmentation[A].In:Proceedings of Image and Vision Computing[C],Auckland,New Zealand,2002:267 ~271.
  • 9Lee D S,Hull J,Erol B.A Bayesian framework for gaussian mixture background modeling[A].In:Proceedings of IEEE International Conference on Image Processing[C],Barcelona,Spain,2003:973 ~ 976.
  • 10Rittscher J,Kato J,Joga S,et al.A probabilistic background model for tracking[A].In:Proceedings of European Conference on Computer Vision[C],Dublin,Ireland,2000,2:336 ~ 350.

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