论文标题
GUSOT：长视频序列的绿色和无监督的单一对象跟踪
GUSOT: Green and Unsupervised Single Object Tracking for Long Video Sequences
论文作者
论文摘要
近年来，依靠深度学习技术的受监督和无监督的深层跟踪器很受欢迎。但是，他们要求高计算复杂性和高内存成本。在这项工作中提出了一个绿色的无监督的单对象跟踪器，称为Gusot，旨在针对资源受限环境下的长视频对象跟踪。 Gusot构建在基线跟踪器UHP-SOT ++上，它适合短期跟踪，其中包含两个附加的新模块：1）丢失的对象恢复，以及2）基于颜色的形状建议。它们有助于解决跟踪损失问题，并分别提供更灵活的对象建议。因此，从长远来看，它们使Gusot能够实现更高的跟踪精度。我们在具有长视频序列的大规模数据集Lasot上进行实验，并表明Gusot提供了轻巧的高性能跟踪解决方案，可在移动和边缘计算平台中找到应用程序。
Supervised and unsupervised deep trackers that rely on deep learning technologies are popular in recent years. Yet, they demand high computational complexity and a high memory cost. A green unsupervised single-object tracker, called GUSOT, that aims at object tracking for long videos under a resource-constrained environment is proposed in this work. Built upon a baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT contains two additional new modules: 1) lost object recovery, and 2) color-saliency-based shape proposal. They help resolve the tracking loss problem and offer a more flexible object proposal, respectively. Thus, they enable GUSOT to achieve higher tracking accuracy in the long run. We conduct experiments on the large-scale dataset LaSOT with long video sequences, and show that GUSOT offers a lightweight high-performance tracking solution that finds applications in mobile and edge computing platforms.
