Adaptive cooperative tracking based on multi-graph embedding and Markov Random Field

Lin Ma, Junliang Xing, Xiaoqin Zhang, Weiming Hu

Abstract: Appearance model is of fundamental importance in a tracking algorithm. In this paper, we propose a new tracking method based on a cooperative object appearance model which incorporates both the discriminative and generative information. We represent the discriminative information with graph embedding (GE). To represent the local object appearance effectively, we divide the object and nearby background into patches. As the discriminative conditions around the 4 object boundaries are different, we divide the patches into 4 groups and perform GE for each group. Markov Random Filed (MRF) is designed to represent the generative information. We propose a novel MRF based method which not only considers the single patch's appearance but also the appearance relations between neighbor patches (not the relations between neighbor patches' states). The proposed cooperative appearance model can represent the object appearance's variation effectively and meanwhile discriminate the object from background robustly. Experimental results on challenging test sequences demonstrated the effectiveness of our method.