Robust discriminative tracking via structured prior regularization

In this paper, we address the problem of tracking an object in a video sequence given its location in the first frame and no other information. Many existing discriminative tracking algorithms usually train a classifier in an on-line manner to separate the object of interest from the background. Slight inaccuracies in the tracking may result in incorrectly labelled training set, which can degrade the tracker. Although a number of approaches such as semi-supervised learning and multiple instance learning have been developed to address this problem, some critical issues still remain unsolved. This work aims to mitigate the shortcomings by exploiting a reliable generative model to support the discriminative learning process. A prior model based on a set of structured Dirichlet-multinomial distributions is proposed to preserve the target's structure information. This prior is then formulated as a regularization term in a training objective function, which casts the tracking task as a prior regularized semi-supervised learning problem. A multi-objective optimization method is developed to search for the solution, taking advantage of a decision maker inside to control the conflicts between different modules. The experiments show that this proposed method outperforms standard algorithms on challenging datasets. It is also demonstrated that the algorithm significantly mitigates the error accumulation effect.