Good tracking performance is in general attributed to accurate representation over previously obtained targets and/or reliable discrimination between the target and the surrounding background. In this work, a robust tracker is proposed by integrating the advantages of both approaches. A subspace is constructed to represent the target and the neighboring background, and their class labels are propagated simultaneously via the learned subspace. In addition, a novel criterion is proposed, by taking account of both the reliability of discrimination and the accuracy of representation, to identify the target from numerous target candidates in each frame. Thus, the ambiguity in the class labels of neighboring background samples, which influences the reliability of the discriminative tracking model, is effectively alleviated, while the training set still remains small. Extensive experiments demonstrate that the proposed approach outperforms most state-of-the-art trackers.

中文翻译：

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通过子空间学习进行视觉跟踪：一种判别方法

良好的跟踪性能通常归因于对先前获得的目标的准确表示和/或目标与周围背景之间的可靠区分。在这项工作中，通过整合两种方法的优点，提出了一种鲁棒的跟踪器。构造一个子空间来表示目标和相邻背景，并且它们的类标签通过学习的子空间同时传播。此外，提出了一种新的标准，通过考虑区分的可靠性和表示的准确性，从每帧的众多目标候选中识别目标。因此，有效地缓解了影响判别跟踪模型可靠性的相邻背景样本类标签的模糊性，而训练集仍然很小。大量实验表明，所提出的方法优于大多数最先进的跟踪器。