Basis pursuit for tracking

R. R. Wang, Y. Q. Chen, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon non-linearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Number of pages4
StatePublished - 2001
EventIEEE International Conference on Image Processing (ICIP) 2001 - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001


OtherIEEE International Conference on Image Processing (ICIP) 2001

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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