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一种电子警察抓拍机快门优先的自适应联动测控方法 被引量:2

Adaptive control linkage system method with shutter priority of electronic police capture machine
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摘要 针对常规抓拍机进行抓拍时关键参数相对固定致使成像图像出现图像模糊、曝光不足或曝光过度等不足,研究了一种电子警察抓拍机快门优先的自适应联动测控方法,以高质量地获取道路机动车流量急剧增加导致道路交通中闯红灯等违章信息。研究结果表明,通过对抓拍机的关键性能参数进行自适应调节,即利用地感线圈测得的车速计算抓拍机的曝光时间及利用抓拍机试拍的图像计算闪光指数和光圈大小并作相应的参数调整,可以实现电子警察抓拍机快门优先的自适应联动控制,从而满足电子警察抓拍机在各种光照环境下均能实现对高速运动的违章车辆的高质量成像,具有重要的实际参考价值。 In order to get high quality information of illegal vehicles in the road transport like running a red light due to road motor vehicle traffic increased dramatically,this paper designed a method of adaptive control linkage system with shutter priority of electronic police capture machine,which will solve the situation of imaging image appears fuzzy,underexposed or overexposed and so on due to key parameter is relatively fixed of conventional capturing machine.The research's results show that though adjusting the key performance parameters of the capture machine adaptively,using ground sense coil measured speed can calculate the exposure time and using capture machine images can calculate and adjust flash index,aperture size and the corresponding adjustment parameter,which can realize the adaptive linkage control of electronic police captured machine with shutter priority.It meets the need that the dectronic police captured machine has to realize high quality imaging of illegal vehicles with high speed movement under various lighting conditions,and it has important practical reference value.
出处 《国外电子测量技术》 2013年第12期31-35,共5页 Foreign Electronic Measurement Technology
基金 国家自然科学基金(61175005)资助项目
关键词 道路交通 电子警察抓拍机 快门优先 自适应联动测控 road transport electronic police capture machine shutter priority adaptive linkage control
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  • 1严新平,张晖,吴超仲,毛喆,雷虎.道路交通驾驶行为研究进展及其展望[J].交通信息与安全,2013,31(1):45-51. 被引量:41
  • 2GUO H, LIN H, ZHANG S,et al. Image-based seatbelt detection[C]. IEEE International Conference onVehicular Electronics and Safety (ICVES),2011.
  • 3FEL2ENSZWALB P F,GIRSHICK R B. Object detectionwith discriminatively trained part-based models[J]. IEEETransactionvs on Pattern Analysis and MachineIntelligence, 2010,32(9) : 1627-1645.
  • 4GOFERMAN S, ZELNIK-MANOR U TAL A. Context-aware saliency detection[J]. IEEE Transactions on PatternAnalysis and Machine Intelligence. 2012,34 (10):1915-1926.
  • 5PAVANI S K,DELGADO D, FRANGI A F. Haar-like features with optimally weighted rectangles forrapid object detection[J]. Pattern Recognition, 2010,43(1) : 160-172.
  • 6CASTRILLoN M, DeNIZ (), HERNdNDEZ D, etal. A comparison of face and facial feature detectorsbased on the Viola-Jones general object detectionframework [ J]. Machine Vision and Applications,2011,22(3) : 481-494.
  • 7DING L, MARTINEZ A M. Features versus context: Anapproach for precise and detailed detection and delineationof faces and facial features [J]. IEEE Transactions onPattern Analysis and Machine Intelligence. 2010,32(11):2022-2038.
  • 8陈玉,严壮志,钱跃竑.基于格子波尔兹曼模型的图像去噪[J].电子学报,2009,37(3):574-580. 被引量:15
  • 9裴玉龙,张需鹏.不良汽车驾驶行为特征分析[J].交通信息与安全,2009,27(3):81-84. 被引量:13
  • 10刘冲,张均东,曾鸿,任光,纪玉龙.基于支持向量机的无穷维AdaBoost算法及其应用[J].仪器仪表学报,2010,31(4):764-769. 被引量:14

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