Can Diversity amongst Learners Improve Online Object Tracking?

  • Georg Nebehay
  • Walter Chibamu
  • Peter R. Lewis
  • Arjun Chandra
  • Roman Pflugfelder
  • Xin Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7872)

Abstract

We present a novel analysis of the state of the art in object tracking with respect to diversity found in its main component, an ensemble classifier that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classification and online learning, and present a detailed evaluation of diversity and performance on benchmark sequences in order to gain an insight into how the tracking performance can be improved.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Georg Nebehay
    • 1
  • Walter Chibamu
    • 2
  • Peter R. Lewis
    • 2
  • Arjun Chandra
    • 3
  • Roman Pflugfelder
    • 1
  • Xin Yao
    • 2
  1. 1.Austrian Institute of TechnologyAustria
  2. 2.CERCIA, School of Computer ScienceUniversity of BirminghamUK
  3. 3.Department of InformaticsUniversity of OsloNorway

Personalised recommendations