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Related Experiment Video

Updated: Apr 23, 2026

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

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

Benjamin Choo¹, Michael Landau², Michael DeVore³

¹Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904, USA. byc6j@virginia.edu.

Sensors (Basel, Switzerland)

|September 20, 2014

Summary

This summary is machine-generated.

This study characterizes Kinect sensor errors, finding depth significantly impacts accuracy. New statistical models offer a more precise understanding of Kinect

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Area of Science:

Robotics and Computer Vision
Sensor Technology
Metrology

Background:

The Kinect sensor is widely used in various applications requiring depth perception.
Accurate characterization of sensor error is crucial for reliable performance.
Existing models may not fully capture the complex error behavior of the Kinect.

Purpose of the Study:

To present stochastic error characteristics of the Kinect sensor across all axis directions (x, y, z).
To develop and validate new measurement and statistics-based models for Kinect error.
To compare the proposed models against existing Kinect error models.

Main Methods:

Depth (z) error measured using a flat surface.
Horizontal (x) and vertical (y) errors measured using a novel 3D checkerboard.
Empirical data collected across the entire field of view for model development.

Main Results:

Kinect measurement error is primarily influenced by object depth, with radial factors also playing a role.
Developed models are based on empirical data, considering pixel location and depth values.
The proposed models provide a more sophisticated and precise characterization of Kinect error distributions.

Conclusions:

The developed models offer improved accuracy in describing Kinect sensor stochastic errors.
Depth and radial factors are key considerations for understanding Kinect error.
This work enhances the reliability of Kinect-based systems through better error modeling.