Learning-based dynamic coupling of discrete and continuous trackers

Gabriel Tsechpenakis, Dimitris Metaxas, Carol Neidle

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.

Original languageEnglish (US)
Pages (from-to)140-156
Number of pages17
JournalComputer Vision and Image Understanding
Issue number2-3 SPEC. ISS.
StatePublished - Nov 2006

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


  • 3D hand tracking
  • ASL recognition
  • Appearance-based discrete tracking
  • Coupling trackers
  • Data-driven tracking error
  • Fingerspelling segmentation
  • Model-based continuous tracking


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