Adaptive Visual Tracking

liuwy@ yingwu@
1
qiningonline@
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.
Order Determination and Sparsity-Regularized Metric Learning for Adaptive Visual Tracking
Nan Jiang 1 , Wenyu Liu 1 , Ying Wu 2 Huazhong University of Sci. & Tech. 2 Northwestern University Wuhan, Hubei, P. R. China. Ev-1-4673-1228-8/12/$31.00 ©2012 IEEE
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case, although the learned metric gives low training error, but it does not perform well in unseen testing data (e.g., from the video frames unseen). And thus it may still produce unsatisfactory results for tracking. The other is the computational cost. A larger M demands a higher computational cost. Some methods use a full projection A ∈ RN ×N . Studies have shown that this treatment is very likely to lead to over-fitting and inefficiency. Thus, it is desirable to determine the optimal order of the projection for best metric adjustment. A naive solution to order determination problem is through trial-and-error experiments, but such a solution is not an option for the tracking applications. To address this problem in a more principled way, we pursue a novel regularization for metric learning. Many existing metric learning methods do not regularize the metric projection. As the metric projection A has a large number of parameters, without a proper regularization, the learning procedure might end up with unwanted solutions. In this paper, we aim to impose a structural regularization of the projection in order to find the optimal order determination. Our basic idea of regularization is to maximize the number of all-zero rows in the projection matrix A. As each row in A is a basis of the projected space, an all-zero row shall eliminate a one-dimensional subspace. Thus the optimal order can be easily identified when eliminating these all-zero rows. This can be viewed as a minimum description length regularization. Formulated via a new sparsity constraint on A, we design gradient-based procedures for solution. We call this new approach Sparsity-Regularized Metric Learning or SRML. This paper contributes to the research of adaptive metric tracking in the following ways. (1) It proposes a general sparsity-regularized approach to the order determination problem in metric learning. This is novel to the research of metric learning. (2) This paper gives a lower-bound of the sparsity constraint, which leads to an analytical form for sparsity regularization. (3) This paper substantializes this sparsity regularization in two solid case studies, and obtains effective methods. (4) This paper integrates the proposed sparsity-regularized metric learning and target tracking. As the proposed tracking method is able to automatically identify the optimal order of the metric to distinguish the target from its distracters, it produces more accurate tracking results and remains to be computationally efficient.
1. Introduction
Having the right matching metric for visual appearances (e.g., visual features and/or their models) is critical for target tracking, especially when the differences between the target and the background is subtle and dynamic. Instead of using predefined and fixed metric, recent attempts have been made to integrate metric learning in tracking [10, 11, 15]. These methods adjust matching metric adaptively, by projecting the original feature space to a new metric space so as to maximize the discrimination between the target and