Order Determination and Sparsity-Regularized Metric Learning Adaptive Visual Tracking

Abstract

Recent attempts of integrating metric learning in visual tracking have produced encouraging results. Instead of using fixed and pre-specified metric in visual appearance matching, these methods are able to learn and adjust the metric adaptively by finding the best projection of the feature space. Such learned metric is by design the best to discriminate the target of interest and its distracters from the background. However, an important issue remained unaddressed is how we can determine the optimal dimensionality of the projection to achieve best discrimination. Using inappropriate dimensions for the projection is likely to result in larger classification error, or higher computational costs and over-fitting. This paper presents a novel solution to this structural order determination problem, by introducing sparsity regularization for metric learning (or SRML). This regularization leads to the lowest possible dimensionality of the projection and thus determining the best order. This can actually be viewed as the minimum description length regularization in metric learning. The experiments validate this new approach on standard benchmark datasets, and demonstrate its effectiveness in visual tracking applications.