Hands Detection Based on Statistical Learning

Hui Li, Lei Yang, Xiaoyu Wu, Jun Zhai
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引用次数: 2

Abstract

This paper proposes a hand detection methodbased on statistical learning training way. Using Microsoft's Kinect sensor, to get the depth information. Through the analysis of the characetristics of hands, put out a kind of new features for statistical learning which approximate with Harr-like feature. The new feature is good at describing complex hand shape degeneration. With the help of Adaboost statistical learning, gets the training model. Experiment results demonstrate that using the new features with Adaboost algorithm can achieve more rapid and robust hands detection system.
基于统计学习的手部检测
本文提出了一种基于统计学习训练方法的手部检测方法。使用微软的Kinect传感器,获取深度信息。通过对手部特征的分析,提出了一种近似于Harr-like特征的统计学习新特征。新的特征可以很好地描述复杂的手形退化。借助Adaboost统计学习,得到训练模型。实验结果表明,将新特征与Adaboost算法相结合,可以实现更加快速、鲁棒的手部检测系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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