Deep learning technology has greatly improved the performance of target tracking, but most recently developed tracking algorithms are short-term tracking algorithms, which cannot meet the actual engineering needs. Based on the Siamese network structure, this paper proposes a long-term tracking framework with a persistent tracking capability. The global proposal module extends the search area globally through the construction of a feature pyramid. The local regression module is mainly responsible for the confidence evaluation of the candidate regions and for performing more accurate bounding box regression. To improve the discriminative ability of the regression network, the error samples are eliminated by synthesizing the temporal information and are then classified through a verification module in advance. Experiments on the VOT long-term tracking dataset and the UAV20L aerial dataset show that the proposed algorithm achieves state-of-the-art performance.

中文翻译：

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基于全球提案网络的长期跟踪框架

深度学习技术大大提高了目标跟踪的性能，但最近开发的跟踪算法都是短期跟踪算法，不能满足实际工程需要。基于Siamese网络结构，本文提出了一种具有持久跟踪能力的长期跟踪框架。全局提案模块通过构建特征金字塔来全局扩展搜索区域。局部回归模块主要负责候选区域的置信度评估和执行更准确的边界框回归。为了提高回归网络的判别能力，通过综合时间信息来消除错误样本，然后预先通过验证模块进行分类。