Aerial Moving Target Tracking using Kernel Density Estimation Based on Particle Filter Algorithm

In this paper, based on resampling particle filter algorithm, a new method to track moving object is proposed. Determining the number of levels needed for resampling in particle filter algorithm, plays an important role in the time duration of each video frame processing. By estimating the Gaussian kernel density the weighted histogram of the target model is obtained and by considering random noise variance at target place the position of candidate particles for the next frame will be predicted. In the current work, the candidate particles are weighted using Bhattacharyya distance, while the number of resampling levels is determined in accordance with the particle weights, adaptively. The radius of the kernel will be matched on moving target variations by using edge detection. Comparison of the result of proposed algorithm with the result of fixed level resampling particle filter algorithm, shows the increasing of moving target detection accuracy up to 88% without excess changes in the particles distribution variances. Moreover, the average process time decreases to 22ms.