改进Kinect人体姿态数据校准与优化，提升测量精度。
Kinect Calibration and Data Optimization For Anthropometric Parameters
- 提出新方法校准Kinect传感器并优化骨骼特征
- 显著提升人体参数测量的稳定性与准确性
- 适合医疗、生物识别等需要精准三维数据的场景
近年来，多种三维视觉系统在医疗和生物识别等领域广泛应用。微软Kinect传感器是其中最常用的设备之一，可获取场景深度图及人体关节三维坐标，便于提取体表参数。然而，从Kinect获取的人体参数和原始关节坐标数据存在不稳定性，主要受个体关节间距和传感器位置影响。因此，未经校准和优化的数据难以保证足够准确与可靠。本研究提出一种新型方法，用于校准Kinect传感器并优化骨架特征。实验结果表明，该方法效果显著，值得在更广泛场景中进一步研究。
原文摘要 · Abstract (English)
Recently, through development of several 3d vision systems, widely used in various applications, medical and biometric fields. Microsoft kinect sensor have been most of used camera among 3d vision systems. Microsoft kinect sensor can obtain depth images of a scene and 3d coordinates of human joints. Thus, anthropometric features can extractable easily. Anthropometric feature and 3d joint coordinate raw datas which captured from kinect sensor is unstable. The strongest reason for this, datas vary by distance between joints of individual and location of kinect sensor. Consequently, usage of this datas without kinect calibration and data optimization does not result in sufficient and healthy. In this study, proposed a novel method to calibrating kinect sensor and optimizing skeleton features. Results indicate that the proposed method is quite effective and worthy of further study in more general scenarios.
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