Kinect Calibration and Data Optimization For Anthropometric Parameters
This addresses data reliability issues for researchers and practitioners using Kinect in medical and biometric applications, but appears incremental as it builds on existing calibration needs.
The study tackled the problem of unstable anthropometric and 3D joint coordinate data from Microsoft Kinect sensors due to variations in distance and sensor location, proposing a novel calibration and optimization method that was found to be quite effective.
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.