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

Xuemin Cheng,Mengdi Xie,Qun Hao

doi:10.3390/s16040486

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.

Highlights

Photographic jitter, caused by the vibration of a moving camera, often produces undesirable effects, which video stabilization methods are designed to mitigate or eliminate
This paper has proposed a comprehensive motion estimation technique for an improved electronic image stabilization (EIS)
Correct points were extracted based on speeded up robust features (SURF) and used to solve for the affine parameters

Summary

Introduction

Photographic jitter, caused by the vibration of a moving camera, often produces undesirable effects, which video stabilization methods are designed to mitigate or eliminate. Image stabilization technologies such as mechanical image stabilization (MIS), optical image stabilization (OIS) [1], and most recently electronic image stabilization (EIS) [2] are widely applied in areas such as camera capture, vehicle monitoring, and airborne and shipboard observations. Compared with MIS and OIS, EIS as a software-based approach has the advantage of lower cost and easier integration, though there are limitations to the software’s accuracy and speed and the hardware’s image performance. For an in-vehicle camera, EIS is relatively cost-efficient and may be the best option

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