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基于色斑联合推举的被遮挡运动目标跟踪 被引量：2

Moving Target Tracking Under Occlusion Based on Multi-hue-blob Voting
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摘要 针对非刚体运动目标被遮挡的跟踪问题,提出一种以目标色斑子块相关匹配联合推举的跟踪方法。利用目标色调特征的不变性对目标色斑进行自适应分块,通过子块的运动状态联合估计目标整体的运动状态,在遮挡发生时判断被遮挡子块,并将其排除到联合推举之外。实验结果表明,该方法可实现对非刚体目标遮挡下的有效跟踪。 A method based on multi-hue-blob correlation matching voting is proposed to solve target tracking problem of the moving target under occlusion efficiently in nonrigid target tracking. It uses invariable hue feature to achieve adaptive blobbing, and gains target＇s motion state through combined estimate of blobs＇ motion states. Experimental results indicate it is robust and has reliable performances under the case of heavy occlusion of the nonrigid moving object.
作者 杨伟 费耀平 李敏
机构地区 中南大学信息科学与工程学院
出处 《计算机工程》 CAS CSCD 北大核心 2009年第4期190-192,共3页 Computer Engineering
关键词 目标跟踪 自适应分块 非刚体 遮挡 target tracking adaptive blobbing nonrigid target occlusion
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献6

  • 1Jepson A D, Fleet D J, Black M J. A Layered Motion Representation with Occlusion and Compact Spatial Support[C]//Proc. of the 7th European Conference on Computer Vision. London, UK: Springer- Verlag, 2002: 692-706.
  • 2Zhou Yue, Tao Hai. A Background Layer Model for Object Tracking Through Occlusion[C]//Proc. of the 9th IEEE International Conference on Computer Vision. Washington D. C., USA: IEEE Computer Society, 2003: 1079-1085.
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同被引文献12

  • 1侯志强,韩崇昭.视觉跟踪技术综述[J].自动化学报,2006,32(4):603-617. 被引量：254
  • 2H T Nguyen, A W M Smeulders. Fast Occluded object tracking by a robust appearance filter[ J], IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 2004,26(8) :1099-1104.
  • 3A Adsm, E Rivlin, I Shimshoni. Robust Fragments-based Track- ing using the Integral Histogram [ C ]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2006. 798-805.
  • 4D Cremers, T Kohlberger, C Schnorr. Non-llnear shape statistics in mumford- shah based segmentation [ C ]. Europeon Conference on Computer Vision, 2002.93-108.
  • 5B K P Horn, B G Schunck. Determining Optical Flow[J]. Artifi- cial Intelligence, 1981,17 : 185-203.
  • 6T Brox, A Bruhn, N Papenberg, J Weiekert. High accuracy opti- cal flow estimation based on a theory for warping[ C]. Europeon Conference on Computer Vision, 2004. 25-36.
  • 7H T Nguyen,A W M Smeulders. Fast Occluded object tracking by a robust appearance filter[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,(08):1099-1104.
  • 8A Adam,E Rivlin,I Shimshoni. Robust Fragments-based Tracking using the Integral Histogram[A].2006.798-805.
  • 9D Cremers,T Kohlberger,C Schnorr. Non-linear shape statistics in mumford-shah based segmentation[A].2002.93-108.
  • 10M Isard,J Maccormick. Bramble:A Bayesian MultipleBlob Tracker[A].2001.34-41.

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计算机工程
2009年 第4期

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