一种基于变换特征和分层模型的静态手势检测方法

本文提出一种基于变换特征和分层模型的静态手势检测方法，所采用的分层模型由一系列手势表观模型和一个总的判别模型构成，其中每个手势表观模型各包含一个通用模板和一系列子类模板。将这些模板作为转移函数，可以从原始的梯度方向直方图特征中得到一组新的特征表示，即变换特征。将此变换特征用于构造分层模型中的判别模型，可以实现背景与手势以及不同手势间的精确分类。为了提高检测速度，算法在初始阶段引入了肤色滤波器方法，用于排除大部分的非肤色区域。实验表明，所述算法能够有效处理视角变换、手势倾斜、自然形变等因素带来的手势表观波动，处理速度可达20帧/秒以上，在鲁棒性和计算效率方面均体现了明显的优势。

A Novel Method for Hand Posture Detection Based on Feature Transform and Hierarchical Model

This paper presents a hand posture detection method based on transform feature representation and hierarchical model. The hierarchical model comprises a series of appearance models and an overall discriminate model. Appearance model for each posture is composed of a general template as well as several sub-category templates. With all the sub-category templates as transition functions, the original gradient histogram features can be converted into a more discriminative representation form. This transform representation is used to construct the discriminative model in the hierarchy model to achieve further posture-background and posture-posture classification. Moreover, to boost the efficiency, a skin-filter is introduced to exclude a wide range of non-skin area. Experimental results show that the proposed algorithm can successfully cope with appearance variability caused by viewpoint changes, posture tilts and natural posture deformation with a detection speed up to 20 frames per second.