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On Feature Combination and Multiple Kernel Learning for Object Tracking

  • Huchuan Lu
  • Wenling Zhang
  • Yen-Wei Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

This paper presents a new method for object tracking based on multiple kernel learning (MKL). MKL is used to learn an optimal combination of \(\mathop \chi \nolimits^2\) kernels and Gaussian kernels, each type of which captures a different feature. Our features include the color information and spatial pyramid histogram (SPH) based on global spatial correspondence of the geometric distribution of visual words. We propose a simple effective way for on-line updating MKL classifier, where useful tracking objects are automatically selected as support vectors. The algorithm handle target appearance variation, and makes better usage of history information, which leads to better discrimination of target and the surrounding background. The experiments on real world sequences demonstrate that our method can track objects accurately and robustly especially under partial occlusion and large appearance change.

Keywords

Object Tracking Visual Tracking Multiple Kernel Multiple Kernel Learn Spatial Pyramid 
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 2011

Authors and Affiliations

  • Huchuan Lu
    • 1
  • Wenling Zhang
    • 1
  • Yen-Wei Chen
    • 1
    • 2
  1. 1.School of Information and Communication EngineeringDalian University of TechnologyDalianChina
  2. 2.College of Information Science and EngineeringRitsumeikan UniversityKusatsuJapan

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