Mean-shift object tracking with discrete and real adaboost techniques

Hendro Baskoro, Jun Seong Kim, Chang-Su Kim

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.

Original languageEnglish
Pages (from-to)282-291
Number of pages10
JournalETRI Journal
Issue number3
Publication statusPublished - 2009 Jun


  • Adaptive boosting (adaboost)
  • Blob tracking
  • Likelihood image
  • Mean-shift
  • Object tracking

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • General Computer Science
  • Electrical and Electronic Engineering


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