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Adaptive Kernel Based Tracking Using Mean-Shift

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Image Analysis and Recognition (ICIAR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4141))

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Abstract

The mean shift algorithm is an kernel based way for efficient object tracking. However, there is presently no clean mechanism for selecting kernel bandwidth when the object size is changing. We present an adaptive kernel bandwidth selection method for rigid object tracking. The kernel bandwidth is updated by using the object affine model that is estimated by using object corner correspondences between two consecutive frames. The centroid of object is registered by a special backward tracking method. M-estimate method is used to reject mismatched pairs (outliers) so as to get better regression results. We have applied the proposed method to track vehicles changing in size with encouraging results.

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Author information

Authors and Affiliations

  1. Electronic Information Engineering College, Henan University of Science and Technology, Luoyang, 471039, China

    Jie-Xin Pu & Ning-Song Peng

Authors
  1. Jie-Xin Pu
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  2. Ning-Song Peng
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Editor information

Editors and Affiliations

  1. Departamento de Engenharia Electrotécnica e de Computadores da, Faculdade de Engenharia da, Universidade do Porto, Campus FEUP, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed S. Kamel

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© 2006 Springer-Verlag Berlin Heidelberg

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Pu, JX., Peng, NS. (2006). Adaptive Kernel Based Tracking Using Mean-Shift. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_37

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  • DOI: https://doi.org/10.1007/11867586_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44891-4

  • Online ISBN: 978-3-540-44893-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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