Dynamic Markov Random Field Model for Visual Tracking

Abstract

We propose a new dynamic Markov random field (DMRF) model to track a heavily occluded object. The DMRF model is a bidirectional graph which consists of three random variables: hidden, observation, and validity. It temporally prunes invalid nodes and links edges among valid nodes by verifying validities of all nodes. In order to apply the proposed DMRF model to the object tracking framework, we use an image block lattice model exactly correspond to nodes and edges in the DMRF model and utilize the mean-shift belief propagation (MSBP). The proposed object tracking method using the DMRF surprisingly tracks a heavily occluded object even if the occluded region is more than 70~80%. Experimental results show that the proposed tracking method gives good tracking performance even on various tracking image sequences(ex. human and face) with heavy occlusion.