Visual tracking with dual modeling

Kwang Moo Yi, Hawook Jeong, Soo Wan Kim, Jin Young Choi

Abstract: In this paper, a new visual tracking method with dual modeling is proposed. The proposed method aims to solve the problems of occlusions, background clutters, and drifting simultaneously with the proposed dual model. The dual model is consisted of single Gaussian models for the foreground and the background. Both models are combined to form a likelihood, which is then efficiently maximized for visual tracking through random sampling and mean-shift. Through dual modeling the proposed method becomes robust to occlusions and background clutters through exclusion of non-target information during maximization of the likelihood. Also, non-target information is unlearned from the foreground model to prevent drifting. The performance of the proposed method is extensively tested against six representative trackers with nine test sequence including two long-term sequences. The experimental results show that our method outperforms all other compared trackers.