+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Statistical analysis-based error models for the Microsoft Kinect(TM) depth sensor



Statistical analysis-based error models for the Microsoft Kinect(TM) depth sensor



Sensors 14(9): 17430-17450



The stochastic error characteristics of the Kinect sensing device are presented for each axis direction. Depth (z) directional error is measured using a flat surface, and horizontal (x) and vertical (y) errors are measured using a novel 3D checkerboard. Results show that the stochastic nature of the Kinect measurement error is affected mostly by the depth at which the object being sensed is located, though radial factors must be considered, as well. Measurement and statistics-based models are presented for the stochastic error in each axis direction, which are based on the location and depth value of empirical data measured for each pixel across the entire field of view. The resulting models are compared against existing Kinect error models, and through these comparisons, the proposed model is shown to be a more sophisticated and precise characterization of the Kinect error distributions.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 055913022

Download citation: RISBibTeXText

PMID: 25237896

DOI: 10.3390/s140917430


Related references

Accuracy of a novel marker tracking approach based on the low-cost Microsoft Kinect v2 sensor. Medical Engineering and Physics 59: 63-69, 2018

Comparative analysis of respiratory motion tracking using Microsoft Kinect v2 sensor. Journal of Applied Clinical Medical Physics 19(3): 193-204, 2018

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis. Sensors 16(7):, 2016

Feasibility of a Customized, In-Home, Game-Based Stroke Exercise Program Using the Microsoft Kinect® Sensor. International Journal of Telerehabilitation 7(2): 23-34, 2015

Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. Conference Proceedings 2011: 1831-1834, 2011

Performance analysis of the Microsoft Kinect sensor for 2D Simultaneous Localization and Mapping (SLAM) techniques. Sensors 14(12): 23365-23387, 2014

Evaluation of the microsoft kinect skeletal versus depth data analysis for timed-up and go and figure of 8 walk tests. Conference Proceedings 2016: 2274-2277, 2016

Developing movement recognition application with the use of Shimmer sensor and Microsoft Kinect sensor. Studies in Health Technology and Informatics 217: 767-772, 2015

Design and Test of a Closed-Loop FES System for Supporting Function of the Hemiparetic Hand Based on Automatic Detection using the Microsoft Kinect sensor. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(8): 1249-1256, 2017

SU-E-I-92: Accuracy Evaluation of Depth Data in Microsoft Kinect. Medical Physics 39(6part5): 3646, 2012

Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE Transactions on Cybernetics 43(5): 1318-1334, 2013

Rapid characterization of vegetation structure with a Microsoft Kinect sensor. Sensors 13(2): 2384-2398, 2013

A depth-based fall detection system using a Kinect® sensor. Sensors 14(2): 2756-2775, 2014

Implementation of facial recognition with Microsoft Kinect v2 sensor for patient verification. Medical Physics 44(6): 2391-2399, 2017

Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs. Computers and Electronics in Agriculture 117: 1-7, 2015