Robust Visual Tracking via Basis Matching

Shengping Zhang*, Xiangyuan LAN, Yuankai Qi, Pong Chi YUEN

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

76 Citations (Scopus)


Most existing tracking approaches are based on either the tracking by detection framework or the tracking by matching framework. The former needs to learn a discriminative classifier using positive and negative samples, which will cause tracking drift due to unreliable samples. The latter usually performs tracking by matching local interest points between a target candidate and the tracked target, which is not robust to target appearance changes over time. In this paper, we propose a novel tracking by matching framework for robust tracking based on basis matching rather than point matching. In particular, we learn the target model from target images using a set of Gabor basis functions, which have large responses on the corresponding spatial positions after a max pooling. During tracking, a target candidate is evaluated by computing the responses of the Gabor basis functions on their corresponding spatial positions. The experimental results on a set of challenging sequences validate that the performance of the proposed tracking method outperforms those of several state-of-The-Art methods.

Original languageEnglish
Article number7428913
Pages (from-to)421-430
Number of pages10
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number3
Publication statusPublished - Mar 2017

Scopus Subject Areas

  • Media Technology
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Gabor filtering
  • max pooling
  • particle filter
  • tracking by matching
  • visual tracking


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