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Building an Effective Template Dictionary for Robust Offline Video Tracking

  • Fei Liu
  • Xiaoqin Zhang
  • Mingyu Fan
  • Di Wang
  • Hongxing Jiang
  • Xiuzi Ye
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 546)

Abstract

Sparse representation is one of most influential framework for visual tracking. However, how to build an effective template dictionary for tracking is less investigated. In this paper, we propose a template dictionary construction method which is effective for offline video tracking. The template dictionary is constructed including several non-polluted templates, and their offsprings. These templates are selectively updated to absorb the appearance variations and prevent the model from drifting. Furthermore, our tracking algorithm is conducted in a bi-directional way, and the optimization process employed in our work is efficiently solved by two-stage sparse representation, which can greatly improve the tracking performance. Experimental results demonstrate that the proposed template dictionary is robust for offline video tracking.

Keywords

Visual tracking Sparse representation Offline video Dictionary learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Fei Liu
    • 1
  • Xiaoqin Zhang
    • 1
  • Mingyu Fan
    • 1
  • Di Wang
    • 1
  • Hongxing Jiang
    • 1
  • Xiuzi Ye
    • 1
  1. 1.College of Mathematics & Information ScienceWenzhou UniversityWenzhouChina

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