Spatiotemporal oriented energy features for visual tracking

  • Authors:
  • Kevin Cannons;Richard Wildes

  • Affiliations:
  • York University, Department of Computer Science and Engineering, Toronto, Ontario, Canada;York University, Department of Computer Science and Engineering, Toronto, Ontario, Canada

  • Venue:
  • ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
  • Year:
  • 2007

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Abstract

This paper presents a novel feature set for visual tracking that is derived from "oriented energies". More specifically, energy measures are used to capture a target's multiscale orientation structure across both space and time, yielding a rich description of its spatiotemporal characteristics. To illustrate utility with respect to a particular tracking mechanism, we show how to instantiate oriented energy features efficiently within the mean shift estimator. Empirical evaluations of the resulting algorithm illustrate that it excels in certain important situations, such as tracking in clutter with multiple similarly colored objects and environments with changing illumination. Many trackers fail when presented with these types of challenging video sequences.