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Sensors 2016, 16(4), 486; doi:10.3390/s16040486

A Comprehensive Motion Estimation Technique for the Improvement of EIS Methods Based on the SURF Algorithm and Kalman Filter

1
Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
2
Beijing Key Lab. for Precision Optoelectronic Measurement Instrument and Technology, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editor: Lianqing Liu
Received: 25 November 2015 / Revised: 31 March 2016 / Accepted: 1 April 2016 / Published: 7 April 2016
(This article belongs to the Section Physical Sensors)

Abstract

Video stabilization is an important technology for removing undesired motion in videos. This paper presents a comprehensive motion estimation method for electronic image stabilization techniques, integrating the speeded up robust features (SURF) algorithm, modified random sample consensus (RANSAC), and the Kalman filter, and also taking camera scaling and conventional camera translation and rotation into full consideration. Using SURF in sub-pixel space, feature points were located and then matched. The false matched points were removed by modified RANSAC. Global motion was estimated by using the feature points and modified cascading parameters, which reduced the accumulated errors in a series of frames and improved the peak signal to noise ratio (PSNR) by 8.2 dB. A specific Kalman filter model was established by considering the movement and scaling of scenes. Finally, video stabilization was achieved with filtered motion parameters using the modified adjacent frame compensation. The experimental results proved that the target images were stabilized even when the vibrating amplitudes of the video become increasingly large. View Full-Text
Keywords: vehicle platform; digital stabilization; SURF; RANSAC; cascade parameters; Kalman filter vehicle platform; digital stabilization; SURF; RANSAC; cascade parameters; Kalman filter
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cheng, X.; Hao, Q.; Xie, M. A Comprehensive Motion Estimation Technique for the Improvement of EIS Methods Based on the SURF Algorithm and Kalman Filter. Sensors 2016, 16, 486.

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