Expectation-Maximization-Based Gaussian Mixture Fusion for Particle Filters and Its Application in Video Tracking

Particle filter (PF) is widely used for dynamic system estimation and tracking applications. However, traditional PF methods often face challenges, including particle degeneracy and insufficient tracking accuracy in complex environments. To address these limitations, this paper introduces a novel fusion method based on the Expectation-Maximization (EM) algorithm. The proposed approach integrates multiple particle filtering techniques to generate several sets of weighted particles at each time step. These sets are then fused into a Gaussian mixture model, providing a more robust and accurate posterior distribution. The fusion process effectively combines the strengths of different particle filters, enhancing tracking performance in dynamic and occlusion-prone video environments. Experimental results demonstrate the superiority of the proposed method in terms of both tracking accuracy and robustness compared to traditional PF-based methods.