Guided optimisation through classification and regression for hand pose estimation

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• 作者：Philip Krejov ; ^{pk00008@surrey.ac.uk} ; Andrew Gilbert ; Richard Bowden
• 关键词：Hand pose estimation ; Human computer interaction ; Hand tracking ; Finger tracking ; Model optimisation ; Random decision forest ; Discriminative learning ; Regression
• 刊名：Computer Vision and Image Understanding
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：155
• 期：Complete
• 页码：124-138
• 全文大小：6524 K
• 卷排序：155

文摘

Proposes hand pose estimation using a combination of model optimisation and discriminative methods which allows tracking to be performed at over 40 frames per second using a single CPU thread. Introduces a residual error regression for hand pose estimation, learning from mistakes in model optimisation. A method of training, which captures system response and user variance, allowing supervised feedback for joint refinement. Extensive quantitative and qualitative evaluation including additional datasets and comparison against multiple state of the art approaches.