Memory-based particle filter for real-time object tracking

Abstract

Particle filter tracking algorithm based on global features becomes invalid when the target's appearance changes or is similar to the background. In order to solve such problems, we propose a memory-based particle filter which considers both local and global feature. Particles provide reliable matching area for local features so that error matching points can be eliminated. Then, local feature points matched to the target will guide the propagation of particles in order to avoid particle degeneration. Experimental results show the tracking effect of the proposed method under various conditions such as scale variation, sudden change of illumination, rotation and so on.