An ant particle filter for visual tracking
Fasheng Wang, Baowei Lin, Xucheng Li · 2017
Sequential Monte Carlo method (also named as particle filter) is now a standard framework for solving nonlinear/non-Gaussian problems, especially in computer vision fields. This paper proposes an ant colony optimization (ACO) based iterative particle filter for visual tracking. In the proposed tracking method, the basic idea of ACO is used to simulate the behavior of particle moving toward the posterior density. Such idea is incorporated into the particle filtering framework in order to overcome the well-known problem of particle impoverishment. We design an iterative proposal distribution for particle generation in order to generate better predicted sample states. The experimental results demonstrate that the proposed tracker shows better performance than the other trackers.