Abstract Existing deep trackers can be roughly divided into either matching-based or classification-based methods. The formers are fast but not very robust; while the latter ones introduce more discriminative information but often very slow. In this work, we present a novel real-time robust tracking method to take full use of the benefits from both kinds of networks. First, we propose a matching-classification network switching (MCS) framework to integrate the matching, classification, verification networks and conduct dynamic switching among them. Second, to speed up online update, we devlop a meta learning method as a critical component in our classification network. The meta classifier is trained offline to obtain general discriminative ability and updated online to the current frame just through one iteration. Extensive experiments are conducted on two popular benchmark datasets. Both qualitative and quantitative evaluations show that our tracker performs favorably against other state-of-the-art trackers with real-time performance.

中文翻译：

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动态匹配分类网络切换的视觉跟踪

摘要 现有的深度跟踪器大致可以分为基于匹配或基于分类的方法。前者很快，但不是很健壮；而后者引入更多的判别信息，但通常很慢。在这项工作中，我们提出了一种新颖的实时鲁棒跟踪方法，以充分利用这两种网络的优势。首先，我们提出了一个匹配分类网络交换（MCS）框架来集成匹配、分类、验证网络并在它们之间进行动态切换。其次，为了加快在线更新，我们开发了一种元学习方法作为我们分类网络中的关键组件。元分类器离线训练以获得一般判别能力，并仅通过一次迭代在线更新到当前帧。在两个流行的基准数据集上进行了广泛的实验。定性和定量评估都表明，我们的跟踪器在实时性能方面优于其他最先进的跟踪器。