Locally Orderless Tracking

Shaul Oron, Aharon Bar-Hillel, Dan Levi, Shai Avidan

Research output: Contribution to journalArticlepeer-review

152 Scopus citations

Abstract

Locally Orderless Tracking (LOT) is a visual tracking algorithm that automatically estimates the amount of local (dis)order in the target. This lets the tracker specialize in both rigid and deformable objects on-line and with no prior assumptions. We provide a probabilistic model of the target variations over time. We then rigorously show that this model is a special case of the Earth Mover’s Distance optimization problem where the ground distance is governed by some underlying noise model. This noise model has several parameters that control the cost of moving pixels and changing their color. We develop two such noise models and demonstrate how their parameters can be estimated on-line during tracking to account for the amount of local (dis)order in the target. We also discuss the significance of this on-line parameter update and demonstrate its contribution to the performance. Finally we show LOT’s tracking capabilities on challenging video sequences, both commonly used and new, displaying performance comparable to state-of-the-art methods.

Original languageEnglish
Pages (from-to)213-228
Number of pages16
JournalInternational Journal of Computer Vision
Volume111
Issue number2
DOIs
StatePublished - 1 Jan 2015
Externally publishedYes

Keywords

  • EMD
  • Noise model
  • Online parameter update
  • Tracking

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Locally Orderless Tracking'. Together they form a unique fingerprint.

Cite this