Online discriminative tracking with active example selection

Most existing discriminative tracking algorithms use a sampling-and-labeling strategy to collect examples and treat the training example collection as a task that is independent of classifier learning. However, the examples collected directly by sampling are neither necessarily informative nor intended to be useful for classifier learning. Updating the classifier with these examples might introduce ambiguity to the tracker. In this paper, we present a novel online discriminative tracking framework that explicitly couples the objectives of example collection and classifier learning. Our method uses Laplacian regularized least squares (LapRLS) to learn a robust classifier that can sufficiently exploit unlabeled data and preserve the local geometrical structure of the feature space. To ensure the high classification confidence of the classifier, we propose an active example selection approach to automatically select the most informative examples for LapRLS. Part of the selected examples that satisfy strict constraints are labeled to enhance the adaptivity of our tracker, which actually provides robust supervisory information to guide semisupervised learning. With active example selection, we are able to avoid the ambiguity introduced by an independent example collection strategy and to alleviate the drift problem caused by misaligned examples. Comparison with the state-of-the-art trackers on the comprehensive benchmark demonstrates that our tracking algorithm is more effective and accurate.