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首页> 外文会议>International Conference on Virtual Reality and Visualization >Automatic Human Body Feature Extraction and Size Measurement by Random Forest Regression Analysis of Geodesics Distance
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Automatic Human Body Feature Extraction and Size Measurement by Random Forest Regression Analysis of Geodesics Distance

机译：大地测量距离的随机森林回归分析自动提取人体特征并进行尺寸测量

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摘要

It is a pervasive problem to obtain the human body size without contacting for apparel application. In this paper, a new approach is proposed which can be applied to calculate human body size such as shoulder width, bust, hips and waist girth with single depth camera. First, single depth camera as the 3D model acquisition device was used to get the 3D human body model. Then an automatic extraction method of focal features on 3D human body via random forest regression analysis of geodesics distance is used to extract the predefined feature points and lines. Finally, the individual human body size is calculated according to the feature points and lines. The method is an automatic data-driven way. The scale-invariant heat kernel signature is exploited to serve as feature proximity. So it is insensitive to postures and different shapes of 3D human body. These main advantages of our method lead to a robust and accurate feature extraction technique and size measurement for 3D human bodies in various postures and shapes. The experiment results show that the average relative error of feature points extraction is 0.0617 cm. The average relative errors of shoulder width and girth are 1.332 cm and 0.7635 cm, respectively. Overall, the algorithm has a better detection effect for 3D human body size, and it is stable with better robustness.
机译：在不接触服装应用的情况下获得人体尺寸是一个普遍的问题。在本文中，提出了一种新方法，该方法可用于通过单深度相机计算人体尺寸，例如肩宽，胸围，臀部和腰围。首先，使用单深度相机作为3D模型获取设备来获取3D人体模型。然后通过对大地测量距离的随机森林回归分析，自动提取3D人体焦点特征的方法来提取预定义的特征点和线。最后，根据特征点和特征线计算出人体的个体大小。该方法是一种自动的数据驱动方式。尺度不变的热核签名被用作特征邻近度。因此，它对3D人体的姿势和不同形状不敏感。我们方法的这些主要优点导致了针对各种姿势和形状的3D人体的鲁棒且准确的特征提取技术和尺寸测量。实验结果表明，特征点提取的平均相对误差为0.0617 cm。肩宽和腰围的平均相对误差分别为1.332 cm和0.7635 cm。总体而言，该算法对3D人体尺寸具有更好的检测效果，并且稳定且具有更好的鲁棒性。

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