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