实时手部检测的实践研究

2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops Pub Date : 2009-12-08 DOI:10.1109/ACII.2009.5349503

J. A. Zondag, T. Gritti, V. Jeanne

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引用次数: 17

摘要

在本文中，我们描述了算法和图像特征，可用于构建实时手检测器。我们使用定向梯度直方图(HOG)特征结合AdaBoost算法的两种变体来展示我们的发现。首先，我们比较了树桩和树的弱分类器。接下来，我们研究大型训练数据库的影响。此外，我们比较了HOG与Haar-like feature的性能。

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Practical study on real-time hand detection

In this paper we describe algorithms and image features that can be used to construct a real-time hand detector. We present our findings using the Histogram of Oriented Gradients (HOG) features in combination with two variations of the AdaBoost algorithm. First, we compare stump and tree weak classifier. Next, we investigate the influence of a large training database. Furthermore, we compare the performance of HOG against the Haar-like features.

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2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops

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