Design of ESS-based Adaptive Particle Filter for Real-time Tracking

Particle filters (PFs) are a set of Bayesian approaches to estimate the posterior densities of the state of the systems from given observations using a set of weighted samples. Due to their superiority in dealing with non-linear and non-Gaussian systems, PFs are widely used in real-time applications such as localization and tracking. However, they are computationally demanding for real-time applications since large number of particles are used. Besides, the filtering performance doesn’t always increase as the number of particles increases, and redundant particles increase the computational complexity. Towards this problem, this paper proposes an adaptive particle filter method which adapts the size of particle set dynamically based on its effective sample size (ESS) under current observations. This method enables effective number of particles used for real-time target tracking while maintaining the tracking quality. Experimental results demonstrate that the proposed method achieves significant particle reduction, which is up to 33% under the same tracking quality compared to the standard PFs, confirming the effectiveness of our proposed method in terms of both tracking performance and speed.