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基于多线索的目标跟踪 被引量:1

Object Tracking Through Multiple Cues
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摘要 通过对人眼跟踪机制的研究,提出了一种新的基于多线索的目标跟踪方法。该方法采用串行结构处理多个视觉线索,首先按近邻原则产生若干候选目标,然后使用不同线索按优先级顺序逐次对候选目标进行筛选,得到的唯一候选目标再经过校正以获得更为准确的跟踪结果。该方法最大的特点是跟踪系统对环境和场景的变化有很强的自组织和自适应能力,系统内多个线索在跟踪过程中的竞争与协同使得跟踪具有强大的适应力和生命力。实验结果表明,该方法显著地提高了跟踪的鲁棒性和准确性。 In this paper a novel approach for object tracking using multiple cues is presented, based on the investigation of human eye tracking. In this approach, multiple cues with serial structure are used in tracking. First, some candidate objects are found through near-neighbor principle. Then multiple cues with different priorities select the candidates in-order. At last, the only survive candidate is fined by the modified module to get more accurate tracking result. The most salient characteristic of this approach is the principles of self-organization and self-adaptation of the changing environment during tracking. Multiple cues compete and cooperate in the system, which make the tracking has strong adaptation and vitality. Experiments show the robustness and accuracy of the tracking algorithm.
出处 《中国图象图形学报》 CSCD 北大核心 2008年第11期2187-2196,共10页 Journal of Image and Graphics
基金 国家自然科学基金委员会与微软亚洲研究院联合资助项目(60672161)
关键词 目标跟踪 多视觉线索 串行结构 Mean SHIFT算法 object tracking, multiple cues, serial structure, Mean Shift algorithm
作者简介 张海青(1977-),男。工程师。中国科学技术大学电子工程与信息科学系硕士研究生。主要研究方向为计算机视觉、智能监控。E-mail:wjzhq@mail.ustc.edu.cn
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参考文献16

  • 1Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift[ A ]. In : Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition[ C], Hilton Head Island, South Carolina, USA, 2000 : 142 - 149.
  • 2Collins R T. Mean-shift blob tracking through scale space [ A ]. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition [ C ] , Madison, WI, USA, 2003 : 234 - 240.
  • 3Lee Mun Wai, Cohen I, Soon Ki Jung. Particle filter with analytical inference for human body tracking [ A ]. In: Proceedings of IEEE Motion and Video Computing[ C ], Florida, USA, 2002 : 159 - 165.
  • 4Ozyildiz E, Krahnstover N, Sharma R. Adaptive texture and color segmentation for tracking moving objects [ J ]. Pattern Recognition, 2002, 35(10) : 2013 - 2029.
  • 5Baumberg A, Hogg D. An efficient method for contour tracking using active shape models [ A ]. In: Proceedings of IEEE Workshop on Motion of Non-rigid and Articulated Objects [ C ] , Austin, Texas, USA, 1994:194 - 199.
  • 6Triesch J, Malsburg C. Self-organized integration of adaptive visual cues for face tracking [ A ]. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition [ C ], Grenoble, France, 2000 : 102 - 109.
  • 7Shen C, Hengel A, Dick A. Probabilistic muhiple cue integration for particle filter based tracking [ A ]. In: Proceedings of the 7th International Conference on Digital Image Computing: Techniques and Applications[C], Sydney, Australia, 2003:399-408.
  • 8Itti L, Koch C. Computational modeling of visual attention [ J ]. Nature Reviews Neuroscience, 2001, 2 (3) : 194 - 230.
  • 9Wolfe J M. Guided search 2.0 a revised model of visual search [J]. Psychonomic Bulletin & Review, 1994, 1 (2) : 202 - 238.
  • 10Friedman-Hill S R, Wolfe J M. Second-order parallel processing:Visual search for the odd item in a subset [ J ]. Journal of Experimental Psychology: Human Perception and Performance, 1995, 21(3) :531 -551.

同被引文献8

  • 1Nummiaro K,Koller-Meier E,Van-Gool L.An adap-tive color-based particle filter[J].Image and VisionComputing,2003,21(1):99-110.
  • 2Comaniciu D,Ramesh V,Meer P.Kernel-based objecttracking[J].IEEE Transactions on Patten Analysisand Machine Intelligence,2003,25(5):564-575.
  • 3Collins R T,Liu Y X,Leordeanu M.Online selectionof discriminative tracking features[J].IEEE Transac-tions on Pattern Analysis and Machine Intelligence,2005,27(10):1631-1643.
  • 4Junqiu Wang,Yasushi Yagi.Integrating color andshape-texture features for adaptive real-time objecttracking[J].IEEE Transactions on Image Processing,2008,17(2):235-241.
  • 5Comaniciu D,Ramesh V,Meer P.Kernel-based objecttracking[J].IEEE Transactions Pattern Analysis andMachine Intelligence,2003,25(5):564-575.
  • 6冈萨雷斯.数字图像处理(第二版)[M].北京:北京电子工业出版社,2007.
  • 7王欢,王江涛,任明武,杨静宇.一种鲁棒的多特征融合目标跟踪新算法[J].中国图象图形学报,2009,14(3):489-498. 被引量:35
  • 8李培华.一种新颖的基于颜色信息的粒子滤波器跟踪算法[J].计算机学报,2009,32(12):2454-2463. 被引量:21

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