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Dynamic Objectness for Adaptive Tracking

  • Severin Stalder
  • Helmut Grabner
  • Luc Van Gool
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

A fundamental problem of object tracking is to adapt to unseen views of the object while not getting distracted by other objects. We introduce Dynamic Objectness in a discriminative tracking framework to sporadically re-discover the tracked object based on motion. In doing so, drifting is effectively limited since tracking becomes more aware of objects as independently moving entities in the scene. The approach not only follows the object, but also the background to not easily adapt to other distracting objects. Finally, an appearance model of the object is incrementally built for an eventual re-detection after a partial or full occlusion. We evaluated it on several well-known tracking sequences and demonstrate results with superior accuracy, especially in difficult sequences with changing aspect ratios, varying scale, partial occlusion and non-rigid objects.

Keywords

Target Object Appearance Model Salient Object Object Region Tracking Approach 
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 2013

Authors and Affiliations

  • Severin Stalder
    • 1
  • Helmut Grabner
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.Computer Vision LaboratoryETH ZurichSwitzerland
  2. 2.ESAT - PSI / IBBTK.U. LeuvenBelgium

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