Fast global motion estimation via iterative least-square method

  • Authors:

  • Jia Wang;Haifeng Wang;Qingshan Liu;Hanqing Lu

  • Affiliations:

  • National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

  • Venue:
  • ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a fast algorithm for global motion estimation based on Iterative Least- Square Estimation (ILSE) technique. Compared with the traditional framework, three improvements were made to accelerate the computation progress. First, a new 3-parameter linear model, together with its solution using modified ILSE method, is proposed to describe and estimate global motion, which is simple and reasonable. Second, a pre-analysis method, Gradient Thresholding (GT) method, is introduced to pre-analyze the image macro-blocks before global motion estimation using their gradient information, which reduce the computational cost by reducing the amount of involved blocks. Lastly, Successive Elimination Algorithm (SEA), which is used to calculate motion field, is improved by a new presented matching criterion considering both the gradient information and the intensity information. The presented method has been tested on a variety of image sequences, and experimental results illustrate its promising performance.