Visual Tracking using the Joint Inference of Target State and Segment-based Appearance Models

In this paper, a robust visual tracking method is proposed by casting tracking as an estimation problem of the joint space of non-rigid appearance model and state. Conventional trackers which use templates as the appearance model do not handle ambiguous samples effectively. On the other hand, trackers that use non-rigid appearance models have low discriminative power and lack methods for restoring methods from inaccurately labeled data. To address this problem, multiple non-rigid appearance models are proposed. The probabilities from these models are effectively marginalized by using the particle Markov chain Monte Carlo framework which provides an exact and efficient approximation of the joint density through marginalization and the theoretical evidences of convergence. An appearance model combines multiple classification results with different features and multiple models can infer an accurate solution despite the failure of several models. The proposed method exhibits high accuracy compared with nine other state-of-the-art trackers in various sequences and the result was analyzed both analyzed both qualitatively and quantitatively.