期刊文献+

利用自适应组合模型实现车辆跟踪 被引量:2

Vehicle Tracking Implemented by Auto-adaptive Combination Model
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摘要 针对视频序列图像目标车辆跟踪中经常因场景光照变化、目标旋转、遮挡等因素导致丢失问题,提出了基于颜色自适应的改进CamShift算法;通过建立凸函数组合模型,利用多目标规划最优求解算法获取自适应颜色识别最佳组合,提高了算法抗干扰能力;利用目标倾角预测识别目标发生形变和旋转,构造多变量状态信息预测目标发生遮挡和瞬间消失,并通过IIR滤波器快速预测目标在下一时刻的运动方式。实验表明,本算法跟踪精度高,鲁棒性强。 Aiming at problems caused by illumination changing of the scene, goal revoking and masking during the video sequence image target tracking, the paper proposes the improved color auto-adaptive CamShift algorithm. Through establishing the convex function combination model, the multi-objective programming optimal algorithm is used to gain the auto-adaptive color and to recognize the best combination. The anti-jamming ability of the algorithm is improved. The target dip is used to forecast the target' s deformation and revolving. The muhivariable status messages are constructed to forecast the masking and instantaneous vanishing of the goal. IIR filter is employed to forecast quickly mode of motion of goal in next moment. The massive experiments indicate that this algorithm has a high tracking accuracy and strong robust.
作者 曲巨宝
出处 《华东交通大学学报》 2010年第4期39-43,共5页 Journal of East China Jiaotong University
基金 福建省教育厅科技项目(JA09240) 武夷学院智能计算网格科研团队项目(2009)
关键词 CAMSHIFT 多模式 自适应 跟踪 倾角 Camshift multi-pattern auto-adaptive track dip
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参考文献10

  • 1石晓瑛,许智榜.车牌自动识别系统设计与实现[J].华东交通大学学报,2009,26(3):48-51. 被引量:11
  • 2CRISAN D,DOUCET A.A survey of convergence results on particle filtering methods for practitioners[J].IEEE Trans Speech and Audio Proc,2002,10(3):173-185.
  • 3李静,陈兆乾.基于颜色的粒子滤波非刚性目标实时跟踪算法[J].郑州大学学报(理学版),2006,38(4):60-63. 被引量:5
  • 4JINMAN K,COHEN I,MEDIONI G.Continuous Tracking within and across Camera Streams[C] //2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2003:267-272.
  • 5BOYLE M.The Effects of Capture Conditions on the CAMSHIFT Face Tracker[R].Alberta,Canada:Department of Computer Science,University of Calgary,2001:45-47.
  • 6NOUAR OULD-DRIS,ALI GANOUN,RAPHAEL CANALS.Improved Object Tracking with CamShift Algorithm[C] //IEEE International Conference on Acoustics,Speech and Signal Procession,2006:165-167.
  • 7NUMIARO K,KOLLER-MEIER E,VAN GOOL L.An adaptive color-based particle filter[J].Image and Vision Computing,2003,21 (1):99-110.
  • 8COMANICIU D,RAMESH V,MEER P.Kemel based object tracking[J].IEEE Trans Pattern Analysis Machine Intelligence,2003,25 (5):564-575.
  • 9ALLEN J G.Object tracking using CanlShift algorithm and multiple quantized feature space[C] //Proceedings of 2003 Pan-Sydney Area Workshop on Visual Information Processing.Darhnghurst,Australia:Australian Computer Society,2004:3-7.
  • 10COMANICIU D,RAMESH V,Meer P.Kernel-based object tracking[J].PAMI,2003,5:564-577.

二级参考文献16

  • 1苏厚胜.车牌识别系统的设计与实现[J].可编程控制器与工厂自动化(PLC FA),2006(3):103-107. 被引量:8
  • 2陈兆学,施鹏飞.基于灰度图像的车牌快速定位和分割方法[J].计算机工程,2006,32(9):173-174. 被引量:16
  • 3刘肃平,陈强.数字图像处理技术在车牌识别中的应用[J].计算机与现代化,2006(8):119-121. 被引量:7
  • 4BIRCHFIELD S T.Elliptical head tracking using intensity gradients and color hitograms[C]//Proceeding of the Computer Vision and Pattern Recognition.Santa Barbara:IEEE Computer Society,1998:232-237.
  • 5ARULAMPALAM M S,MASKELL S,GORDON N,et al.A tutorial on particle filters for online nonlinear/nonGaussian Bayesian tracking[J].IEEE Transactions on Signal Processing,2002,(2):174-188.
  • 6LIU J S,CHEN R.Sequential Monte Carlo methods for dynamic systems[J].Journal of the American Statistical Association,1998,93(443):1032-1044.
  • 7COMANICIU D,RAMESH V,MEER P.Real-time tracking of non-rigid objects using mean shift[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2000:142-149.
  • 8NUMMIARO K,KOOLER-MEIER E,GOOL L V.A color-based particle filter[C]//Proceedings of the 1st International Workshop on Generative-model-based Vision,in Conjunction with ECCV'02.2002,1:53-60.
  • 9PEREZ R P,HUE C,VERMAAK J,et al.Color-based probabilistic tracking[C]//Proceedings of the European Conference on Computer Vision,2002:661-675.
  • 10OKUMA K,TALEGHANI A,FREITAS N D,et al.A boosted particle filter:multitarget detection and tracking[EB/OL].http://www.cs.ubc.ca/~okumak.

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