Implementation of Particle Filter Visual Object Tracking (PF), Kalman Particle Filter Visual Object Tracking (KPF), and Unscented Particle Filter Visual Object Tracking (UPF)

This code implements three sophisticated visual object tracking algorithms: Particle Filter (PF), Kalman Particle Filter (KPF), and Unscented Particle Filter (UPF). These algorithms constitute my core development work over the past two years, designed to achieve robust visual target tracking. The implementation demonstrates superior robustness and performance compared to conventional tracking methods like MeanShift and Camshift, primarily through advanced probability density estimation and state prediction mechanisms.

Notably, the KPF and UPF implementations for visual target tracking represent significant original work - these are not available in public repositories. The KPF algorithm integrates Kalman filtering with particle sampling for improved state estimation, while UPF employs unscented transformation for better importance sampling. Although current versions include only partial code optimization, the enhanced variants have been successfully integrated into our research group's active visual target tracking and engagement platform. We're sharing these implementations to support the research and development community, hoping they'll prove valuable for your projects.