Robust Hand Posture Recognition Integrating Multi-cue Hand Tracking

详细信息    查看全文

• 作者：Chuanbo Weng ; Yang Li ; Mingmin Zhang ; Kangde Guo ; Xing Tang ; et al.
• 关键词：Hand posture recognition ; Hand tracking ; Multi ; cue method ; Density distribution feature
• 刊名：Lecture Notes in Computer Science
• 出版年：2010
• 出版时间：2010
• 年：2010
• 卷：6249
• 期：1
• 页码：497-508
• 全文大小：684.0 KB

文摘

This paper proposes a robust real-time method for hand tracking and hand posture recognition. Dealing with complex background, scale-invariance and rotation-invariance are the difficulties for hand posture recognition. To solve these difficulties, we firstly detect the specific posture using the method based on Modified Census Transform, in order to trigger hand tracking and hand posture recognition. For the complex background particularly with large skin-color alike objects, a multi-cue method, based on velocity weighted features and color cue, is proposed to deal with the hand tracking. Then we segment the hand using both Bayesian skin-color model and the hand tracking result. Finally, we use a novel method based on density distribution feature to recognize hand posture. It largely enforces the robustness of hand posture recognition because of scale-invariance and rotation-invariance. Experiment results and applications demonstrate the effectiveness of our method.