Unconstrained Multiple-People Tracking

  • Daniel Rowe
  • Ian Reid
  • Jordi Gonzàlez
  • Juan Jose Villanueva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


This work presents two main contributions to achieve robust multiple-target tracking in uncontrolled scenarios. A novel system which consists on a hierarchical architecture is proposed. Each level is devoted to one of the main tracking functionalities: target detection, low-level tracking, and high-level tasks such as target-appearance representation, or event management. Secondly, tracking performances are enhanced by on-line building and updating multiple appearance models. Successful experimental results are accomplished on sequences with significant illumination changes, grouping, splitting and occlusion events.


Target Appearance Occlusion Event Bhattacharyya Distance Feature Pool Blob Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniel Rowe
    • 1
  • Ian Reid
    • 2
  • Jordi Gonzàlez
    • 3
  • Juan Jose Villanueva
    • 1
  1. 1.Computer Vision CentreUniversitat Autònoma de BarcelonaSpain
  2. 2.Active Vision LabOxford UniversityUnited Kingdom
  3. 3.Institut de Robòtica i Informàtica Industrial, UPCBarcelonaSpain

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