Tracking Moving Objects Using Adaptive Weighted Histogram Matching Algorithm Based on Particle Filter

Estimate the position of moving objects tracking is an important and Many algorithms have been proposed. In this paper, a method to estimate the position of moving objects by solving the Bayesian equations of nonlinear systems with non-Gaussian distributed algorithm based on particle filter are offered. In this way will build the first target model of weighted histogram, Then applying random noise in the location of the first frame image, predicted the candidate particles in the next step and build a histogram weighted by the candidate particles and particles Start by Bhattacharya distance weighting on the similarity between the target model and candidate model particles and estimated the target position in the next frame by the resampling algorithm in the particle filter, Finally an adaptive target model update is performed, if necessary, based on the best model for particle similar to the target.