Advanced Search
Volume 32 Issue 7
Aug.  2010
Turn off MathJax
Article Contents
Wang Yu-xiong, Zhang Yu-jin, Wang Xiao-hua. Mean-Shift Object tracking through 4-D Scale Space[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1626-1632. doi: 10.3724/SP.J.1146.2009.01000
Citation: Wang Yu-xiong, Zhang Yu-jin, Wang Xiao-hua. Mean-Shift Object tracking through 4-D Scale Space[J]. Journal of Electronics & Information Technology, 2010, 32(7): 1626-1632. doi: 10.3724/SP.J.1146.2009.01000

Mean-Shift Object tracking through 4-D Scale Space

doi: 10.3724/SP.J.1146.2009.01000
  • Received Date: 2009-07-14
  • Rev Recd Date: 2009-11-14
  • Publish Date: 2010-07-19
  • The scale self-adaptive mechanism is one of the promising research directions in the object tracking issue based on mean-shift. A typical method is to adopt Lindebergs scale-space theory to obtain the scale information of the target. However, the 2-D scale vector is reduced to 1-D in the existing algorithm. So it is not precise enough to portray the changing state of the object scale under affine transformation, which greatly limits the scope of application of the method. Hence, the 1-D filter in the scale dimension is extended to 2-D in this paper. Correspondingly, the 4-D scale space is constructed, then the problem is mapped into two interleaved mean-shift procedure in the spatial and scale dimension through scale space. The modified algorithm achieves the effective object tracking in spatial position and scale direction simultaneously, which enhances the self adaptability when the target scale is changing, and expands the scope of application of the algorithm.
  • loading
  • Cheng Y. Mean shift, mode seeking, and clustering [J].IEEETransactions on Pattern Analysis and Machine Intelligence.1995, 17(8):790-799[2]Comaniciu D, Ramesh V, and Meer P. Real-time tracking ofnon-rigid objects using mean shift [C]. IEEE Conference onComputer Vision and Pattern Recognition, Hilton Head, SC,USA, 2000, II: 142-149.[3]Yilmaz A. Object tracking by asymmetric kernel mean shiftwith automatic scale and orientation selection [C]. IEEEConference on Computer Vision and Pattern Recognition,Minneapolis, MN, USA, 2007: 1-6.[4]Yao An-bang, Wang Gui-jin, Lin Xing-gang, and Wang Hao.Kernel based articulated object tracking with scaleadaptation and model update [C]. IEEE InternationalConference on Acoustics, Speech and Signal Processing, LasVegas, NV, USA, 2008: 945-948.Chen Xiao-peng, Zhou You-xue, Huang Xiao-san, and LiCheng-rong. Adaptive bandwidth mean shift object tracking[C]. IEEE Conference on Robotics, Automation andMechatronics, Chengdu, China, 2008: 1011-1017.Jiang Zhuo-lin, Li Shao-fa, Jia Xi-ping, and Zhu Hong-li. Animproved mean shift tracking method based onnonparametric clustering and adaptive bandwidth [C].Proceedings of the Seventh International Conference onMachine Learning and Cybernetics, Kunming, China, 2008:12-15.[5]Collins R T. Mean-shift blob tracking through scale space [C].IEEE Conference on Computer Vision and PatternRecognition, Baltimore, USA, 2003, 2: 234-240.[6]彭宁嵩, 杨杰, 刘志, 张凤超. Mean Shift 跟踪算法中核函数窗宽的自动选取 [J]. 软件学报, 2005, 16(9): 1542-1550.Peng Ning-song, Yang Jie, Liu Zhi, and Zhang Feng-chao.Automatic selection of kernel-bandwidth for Mean-Shiftobject tracking [J]. Journal of Software, 2005, 16(9):1542-1550.[7]章毓晋. 图像工程. 上册, 图像处理 [M]. 第2 版, 北京: 清华大学出版社, 2006: 62-71.Zhang Yu-jin. Image Engineering (I), Image Processing [M].Second Edition, Beijing: Tsinghua University Press, 2006:62-71.[8]Lindeberg T. Scale-Space Theory in Computer Vision [M].Netherlands: Kluwer Academic Publisher, 1994, Chapter 13.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (4055) PDF downloads(887) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return