Adaptive Kernel Radius in Estimating the Position of Moving Target Tracking based on Resampling Particle Filter Algorithm

In this paper, we perform adaptive radius of the kernel by an edge detection method with tracking algorithm based on kernel density and provides a robust tracking algorithm, in combination with the resampling particle filter algorithm. In the first frame, by suitable kernel density estimation, is obtained the weighted histogram of the target model and by adding random noise variance at this place, are predicted the position of candidate particles in the next step. The weighted histogram of this candidate particles are compared with the same density kernel by the target model and are weighted the candidate particles by Bhattacharyya distance. The resampling algorithm, estimates the target position in the next frame. Finally, radius of the kernel is consistent with the target model changes. If needed, the target model is updated according to the best model of particle similar to the target model, adaptively.