论文标题
DMV：视觉对象通过零件级密集的内存和基于投票的检索跟踪
DMV: Visual Object Tracking via Part-level Dense Memory and Voting-based Retrieval
论文作者
论文摘要
我们通过零件级密集的内存和基于投票的检索（称为DMV）提出了一种新颖的基于内存的跟踪器。由于已经将深度学习技术引入了跟踪场，因此由于速度和准确性之间的平衡，暹罗跟踪器吸引了许多研究人员。但是，它们中的大多数基于单个模板匹配，该模板限制了性能，因为它将可访问的内形式限制为初始目标特征。在本文中，我们通过维护保存跟踪记录的外部内存来减轻此限制。从内存中零件级的检索还可以从模板中解放出信息，并允许我们的跟踪器更好地处理诸如外观变化和遮挡之类的挑战。通过在跟踪过程中更新内存，无需在线学习就可以增强目标对象的代表性。我们还提出了一种新颖的投票机制，用于记忆阅读，以滤除内存中不可靠的信息。我们全面评估了在OTB-100，TrackingNet，GoT-10k，Lasot和UAV123上的跟踪器，这表明我们的方法与最先进的方法相当。
We propose a novel memory-based tracker via part-level dense memory and voting-based retrieval, called DMV. Since deep learning techniques have been introduced to the tracking field, Siamese trackers have attracted many researchers due to the balance between speed and accuracy. However, most of them are based on a single template matching, which limits the performance as it restricts the accessible in-formation to the initial target features. In this paper, we relieve this limitation by maintaining an external memory that saves the tracking record. Part-level retrieval from the memory also liberates the information from the template and allows our tracker to better handle the challenges such as appearance changes and occlusions. By updating the memory during tracking, the representative power for the target object can be enhanced without online learning. We also propose a novel voting mechanism for the memory reading to filter out unreliable information in the memory. We comprehensively evaluate our tracker on OTB-100,TrackingNet, GOT-10k, LaSOT, and UAV123, which show that our method yields comparable results to the state-of-the-art methods.
