Tracking based on unit-linking pulse coupled neural network image icon and particle filter

Abstract

Visual tracking is a challenging problem in computer vision. Many visual trackers either rely on luminance information or other simple color representations for image description. This paper introduces a tracking algorithm using unit-linking PCNN (Pulse Coupled Neural Network) image icon and particle filter. This approach has the translation, rotation, and scale invariance for using unit-linking PCNN image icon as the features. The experimental results show the proposed approach is with 16.43% higher median distance precision than the color gradient-based tracker. This unit-linking PCNN image icon-based particle filter tracker can better solve the problems caused by partial occlusions, or out-of-plane rotation, or scale variation, or non-rigid object deformation, or fast motion.