The Improved Particle Filter for Motion Estimation

Abstract

In this paper, we used particle filter to motion estimation algorithm on real-time for mobile surveillance robot. Particle filter based on the Monte Carlo's sampling method, be used Bayesian conditional probability model which having prior distribution probability and posterior distribution probability. By using particle filter, it can be possible to tracking and estimating robustly for object's motion and movement. Also most of the initial probability density was set to define or random manually. Proposed method in this paper, however, using the Sum of Absolute Differences (SAD) is to take the initial probability density. Therefore, by using a particle filter to the object tracking system, it can be configured more efficient.