title: A Comprehensive Motion Estimation Technique for the Improvement of EIS Methods Based on the SURF Algorithm and Kalman Filter.
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.
authors.affiliation: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China. cheng-xm@mail.tsinghua.edu.cn.
authors.forename: Xuemin
authors.initials: X
authors.lastname: Cheng
authors.affiliation: Beijing Key Lab. for Precision Optoelectronic Measurement Instrument and Technology, School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China. qhao@bit.edu.cn.
authors.forename: Qun
authors.initials: Q
authors.lastname: Hao
authors.affiliation: Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China. xie.mengdi12@alum.sz.tsinghua.edu.cn.
authors.forename: Mengdi
authors.initials: M
authors.lastname: Xie
keywords: Kalman filter; RANSAC; SURF; cascade parameters; digital stabilization; vehicle platform