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.

翻译：近年来，随着多种三维视觉系统在医疗和生物识别等领域的广泛应用，微软Kinect传感器已成为三维视觉系统中使用最广泛的摄像头之一。微软Kinect传感器能够获取场景的深度图像以及人体关节的三维坐标，从而便于提取人体测量特征。然而，从Kinect传感器捕获的人体测量特征和三维关节坐标原始数据存在不稳定性，其主要原因在于数据会因个体关节间距离及Kinect传感器位置的变化而产生波动。因此，若未经Kinect标定与数据优化而直接使用这些数据，将无法获得充分且可靠的结果。本研究提出了一种新颖的Kinect传感器标定与骨架特征优化方法。实验结果表明，所提方法具有显著效果，值得在更广泛的场景中进行深入研究。