“略读”跟踪：一种实时的鲁棒长期跟踪框架
'Skimming-Perusal' Tracking: A Framework for Real-Time and Robust Long-term Tracking
摘要：
与传统的短期跟踪相比，长期跟踪提出了更多的挑战，并且更接近现实应用。然而，很少有工作已经完成，他们的表现也受到限制。在这项工作中，我们提出了一种新的鲁棒性和实时的【形】长期跟踪框架的基础上提出的略读和细读模块。阅读模块由一个有效的限界框回归器和一个鲁棒的目标验证器组成，前者用于生成一系列候选建议，后者用于根据其置信度来推断最佳候选。基于该分数，我们的跟踪器确定跟踪对象是否存在，然后在下一帧中分别选择局部搜索或全局搜索的跟踪策略。为了加快图像范围内的全局搜索，设计了一种新的略读模块，以有效地选择最可能的区域从大量的滑动窗口。在VOT-2018长期和OxUvA长期基准上的大量实验结果表明，所提出的方法实现了最佳性能并实时的【形】运行。源代码可在https://github.com/iiau-tracker/SPLT上获得。
英文摘要：
Compared with traditional short-term tracking, long-term tracking poses more challenges and is much closer to realistic applications. However, few works have been done and their performance have also been limited. In this work, we present a novel robust and real-time long-term tracking framework based on the proposed skimming and perusal modules. The perusal module consists of an effective bounding box regressor to generate a series of candidate proposals and a robust target verifier to infer the optimal candidate with its confidence score. Based on this score, our tracker determines whether the tracked object being present or absent, and then chooses the tracking strategies of local search or global search respectively in the next frame. To speed up the image-wide global search, a novel skimming module is designed to efficiently choose the most possible regions from a large number of sliding windows. Numerous experimental results on the VOT-2018 long-term and OxUvA long-term benchmarks demonstrate that the proposed method achieves the best performance and runs in real-time. The source codes are available at https://github.com/iiau-tracker/SPLT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
