International Journal of Computer Vision

, Volume 95, Issue 2, pp 154–179 | Cite as

Linear Regression and Adaptive Appearance Models for Fast Simultaneous Modelling and Tracking

  • Liam Ellis
  • Nicholas Dowson
  • Jiri Matas
  • Richard Bowden
Article

Abstract

This work proposes an approach to tracking by regression that uses no hard-coded models and no offline learning stage. The Linear Predictor (LP) tracker has been shown to be highly computationally efficient, resulting in fast tracking. Regression tracking techniques tend to require offline learning to learn suitable regression functions. This work removes the need for offline learning and therefore increases the applicability of the technique. The online-LP tracker can simply be seeded with an initial target location, akin to the ubiquitous Lucas-Kanade algorithm that tracks by registering an image template via minimisation.

A fundamental issue for all trackers is the representation of the target appearance and how this representation is able to adapt to changes in target appearance over time. The two proposed methods, LP-SMAT and LP-MED, demonstrate the ability to adapt to large appearance variations by incrementally building an appearance model that identifies modes or aspects of the target appearance and associates these aspects to the Linear Predictor trackers to which they are best suited. Experiments comparing and evaluating regression and registration techniques are presented along with performance evaluations favourably comparing the proposed tracker and appearance model learning methods to other state of the art simultaneous modelling and tracking approaches.

Keywords

Regression tracking Online appearance modelling 

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Liam Ellis
    • 1
    • 2
  • Nicholas Dowson
    • 3
  • Jiri Matas
    • 4
  • Richard Bowden
    • 2
  1. 1.CVLLinköping UniversityLinköpingSweden
  2. 2.CVSSPUniversity of SurreyGuildfordUK
  3. 3.AEHRCRoyal Brisbane and Women’s HospitalBrisbaneAustralia
  4. 4.CMPCzech Technical UniversityPragueCzech Republic

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