一种多体感人体步态序列合并方法

Xiaojuan He, Chengcheng Chen, Hanzhen Zhang, Yuehu Liu
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引用次数: 1

摘要

Kinect作为一种动作捕捉设备,可以从深度图像中预测人体25个关节的时间三维位置,因此被广泛应用于步态分析、步态识别和临床医学等诸多领域。然而,单个Kinect的采集范围非常有限,采集的步态数据很少,这将大大降低步态分析和识别的准确性和可靠性。为了解决这一问题,我们提出了一种新的多kinect人体步态序列合并方法。它不仅可以扩大数据采集范围,增加步态数据序列的长度,而且由于它是一种非侵入性、非接触式的数据采集方法,可以避免在跑步机上行走带来的不良影响。首先,我们引入kinect的最佳采集范围,以提高步态序列测量的准确性。其次,直接利用结合点的深度值进行坐标变换,既减小了转换误差,又便于计算;最后,在合并步态序列时,利用所有步态序列得到稳定、有效、长距离的步态序列。我们设计了相关实验,将合并后的步态序列与单个Kinect采集的步态序列进行对比,结果验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Human Gait Sequence Merging Method For Multi-kinect
As a motion capture device, Kinect can predict temporal 3D positions of 25 body joints from depth image, so it is widely used in many field such as gait analysis, gait recognition and clinical medicine. However, the collection range of a single Kinect is very limited and it only collect few gait data, which will greatly reduce the accuracy and reliability of the gait analysis and recognition. In order to solve this problem, we propose a new human gait sequence merging method for multi-Kinect. It can not only extend the data acquisition range and increase the length of gait data sequence, but also avoid the bad effects of walking on a treadmill because it is a non-invasive, non-contact data collection method. Firstly, we first introduced the optimal collection range of kinect to improve the accuracy of gait sequence measurements. Secondly, we directly use the depth value of the joint point for coordinate transformation, which not only reduces the conversion error but also make the calculation easy. Finally, when gait sequences are merged, we utilize all the gait sequences to obtain stable, effective and long-distance gait sequences. We designed relevant experiments to compare the merged gait sequence with the gait sequence collected by single Kinect, and the results verified the validity of the method.
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