Hand Posture Estimation in Complex Backgrounds by Considering Mis-match of Model

This paper proposes a novel method of estimating 3-D hand posture from images observed in complex backgrounds. Conventional methods often cause mistakes by mis-matches of local image features. Our method considers possibility of the mis-match between each posture model appearance and the other model appearances in a Baysian stochastic estimation form by introducing a novel likelihood concept “Mistakenly Matching Likelihood (MML)“. The correct posture model is discriminated from mis-matches by MML-based posture candidate evaluation. The method is applied to hand tracking problem in complex backgrounds and its effectiveness is shown.

• Type: Conference
• Year: 2007
• Where: ACCV
• Authors: Akihiro Imai, Nobutaka Shimada, Yoshiaki Shirai
• Keywords: 3-D Hand Posture, ACCV 2007, Computer Vision, Posture Model, Posture Model Appearance