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A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration

  • Yang Li
  • Jianke ZhuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there is still a need to improve the overall tracking capability. In this paper, we presented a very appealing tracker based on the correlation filter framework. To tackle the problem of the fixed template size in kernel correlation filter tracker, we suggest an effective scale adaptive scheme. Moreover, the powerful features including HoG and color-naming are integrated together to further boost the overall tracking performance. The extensive empirical evaluations on the benchmark videos and VOT 2014 dataset demonstrate that the proposed tracker is very promising for the various challenging scenarios. Our method successfully tracked the targets in about 72% videos and outperformed the state-of-the-art trackers on the benchmark dataset with 51 sequences.

Keywords

Visual Tracking Correlation Filter Kernel Learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of Computer ScienceZhejiang University ZhejiangHangzhouChina

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