Visual object tracking via iterative ant particle filtering
Fasheng Wang, Yanbo Wang, Jianjun He, Fuming Sun, Xucheng Li, Junxing Zhang · IET Image Processing · 2020
Visual object tracking remains a challenging task in computer vision although important progress has been made in the past decades. Particle filter (PF) is now a standard framework for solving non‐linear/non‐Gaussian problems, especially in visual object tracking. This study proposes an ant colony optimisation (ACO)‐based iterative PF for object tracking. In the proposed method, the basic idea of ACO is used to simulate the behaviour of a particle moving toward the posterior distribution. Such idea is incorporated into the particle filtering framework in order to overcome the well‐known particle impoverishment problem. An iterative unscented Kalman filter is used to design a proposal distribution for particle generation in order to generate better predicted sample states. For the likelihood model, the authors adopt the locality sensitive histogram to model the appearance of the target object, which can better handle the illumination variation during tracking. The experimental results demonstrate that the proposed tracker shows better performance than the other tracking methods.