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Kernel-Bandwidth Adaptation for Tracking Object Changing in Size

  • Ning-Song Peng
  • Jie Yang
  • Jia-Xin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3212)

Abstract

In the case of tracking object changing in size, traditional mean-shift based algorithm always leads to poor localization owing to its unchanged kernel-bandwidth. To overcome this limitation, a novel kernel-bandwidth adaptation method is proposed where object affine model is employed to describe scaling problem. With the registration of object centroid in consecutive frames by backward tracking, scaling magnitude in the affine model can be estimated with more accuracy. Therefore, kernel-bandwidth is updated with respect to the scaling magnitude so as to keep up with variety of object size. We have applied the proposed method to track vehicles changing in size with encouraging results.

Keywords

Consecutive Frame Affine Model Poor Localization Scaling Magnitude Bhattacharyya Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ning-Song Peng
    • 1
    • 2
  • Jie Yang
    • 1
  • Jia-Xin Chen
    • 2
  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiaotong UniversityShanghaiChina
  2. 2.Institute of Electronic and InformationHenan University of Science and TechnologyLuoyangChina

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