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Real-Time Human Motion Analysis Based on Analysis of Silhouette Contour and Color Blob

机译:基于轮廓轮廓和色斑分析的实时人体运动分析

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摘要

This paper presents real-time human motion analysis for human-machine interface. In general, man-machine 'smart' interface requires real-time human motion capturing systems without special devices or markers. Although vision-based human motion capturing systems do not use such special devices and markers, they are essentially unstable and can only acquire partial information because of self-occlusion. When we analyze full-body motion, the problem becomes severer. Therefore, we have to introduce a robust pose estimation strategy to deal with relatively poor results of image analysis. To solve this problem, we have developed a method to estimate full-body human postures, where an initial estimation is acquired by real-time inverse kinematics and, based on the estimation, more accurate estimation is searched for referring to the processed image. The key points are that our system combines silhouette contour analysis and color blob analysis for feature extraction to achieve robust feature extraction and that our system can estimate full-body human postures from limited perceptual cues such as positions of a head, hands and feet, which can be stably acquired by feature extraction process. In this paper, we outline a real-time and on-line human motion analysis system.
机译:本文提出了人机界面的实时人体运动分析。通常,人机“智能”界面需要实时的人体动作捕捉系统,而无需特殊的设备或标记。尽管基于视觉的人体运动捕捉系统没有使用这种特殊的设备和标记,但是它们本质上是不稳定的,并且由于自身的遮挡而只能获取部分信息。当我们分析全身运动时,问题变得更加严重。因此,我们必须引入一种鲁棒的姿态估计策略来处理相对较差的图像分析结果。为了解决该问题,我们开发了一种估计全身人体姿势的方法,其中通过实时逆运动学获取初始估计,并且基于该估计,搜索更准确的估计以参考处理后的图像。关键点在于我们的系统将轮廓轮廓分析和颜色斑点分析相结合以进行特征提取,以实现可靠的特征提取,并且我们的系统可以从有限的感知线索(例如头,手和脚的位置)中估计人体的整个姿势,可以通过特征提取过程稳定地获取。在本文中,我们概述了实时的在线人体运动分析系统。

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