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JBE, vol. 25, no. 5, pp.808-811, September, 2020

DOI: https://doi.org/10.5909/JBE.2020.25.5.808

Noise Reduction Method Using Randomized Unscented Kalman Filter for RGB+D Camera Sensors

Oh-Seol Kwon

C.A E-mail: osk1@changwon.ac.kr

Abstract:

This paper proposes a method to minimize the error of the Kinect camera sensor by using a random undirected Kalman filter. Kinect cameras, which provide RGB values and depth information, cause nonlinear errors in the sensor, causing problems in various applications such as skeleton detection. Conventional methods have tried to remove errors by using various filtering techniques. However, there is a limit to removing nonlinear noise effectively. Therefore, in this paper, a randomized unscented Kalman filter was applied to predict and update the nonlinear noise characteristics, we next tried to enhance a performance of skeleton detection. The experimental results confirmed that the proposed method is superior to the conventional method in quantitative results and reconstructed images on 3D space.



Keyword: Kinetic camera, RUKF, and skeleton detection

Reference:
[1] Microsoft Robotics. Kinect Sensor. from http://msdn.microsoft.com /en-us/library/hh438998.aspx. (accessed Oct. 06, 2017)
[2] G. Han, I. Lee, H. and Choi, "MPEG-U based advanced user interaction interface system using hand posture recognition," The Korean Institute of Broadcast and Media Engineers, Vol. 19, No. 1, pp. 83-95, 2014.
[3] M. Goulao, "Kinematic data filtering with unscented kalman filter," Thesis of Tecnico Lisboa, 2017.
[4] A. Baghdadi, L. Cavuoto, and J. Crassidis, “Hip and trunk kinematics estimation in gait through kalman filter using IMU data at the ankle,” IEEE Sensors Journal, Vol. 18, No. 10, pp. 4253-4259, 2018.
[5] O. Straka, J. Dunik, and M. Simandl, “Randomized unscented kalman filter in target tracking,” IEEE International Conference on Information Fusion, Singapore, pp. 503-510, July 2012.

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