Model-based hand tracking by chamfer distance and adaptive color learning using particle filter

Chutisant Kerdvibulvech, Hideo Saito

Research output: Contribution to journalArticle

11 Citations (Scopus)


We propose a new model-based hand tracking method for recovering of three-dimensional hand motion from an image sequence. We first build a three-dimensional hand model using truncated quadrics. The degrees of freedom (DOF) for each joint correspond to the DOF of a real hand. This feature extraction is performed by using the Chamfer Distance function for the edge likelihood. The silhouette likelihood is performed by using a Bayesian classifier and the online adaptation of skin color probabilities. Therefore, it is to effectively deal with any illumination changes. Particle filtering is used to track the hand by predicting the next state of three-dimensional hand model. By using these techniques, this method adds the useful ability of automatic recovery from tracking failures. This method can also be used to track the guitarist's hand.

Original languageEnglish
Article number724947
JournalEurasip Journal on Image and Video Processing
Publication statusPublished - 2009


ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Information Systems

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