Zhao Haojie, Yan Bin, Wang Dong, Qian Xuesheng, Yang Xiaoyun, Lu Huchuan
IEEE Trans Pattern Anal Mach Intell. 2023 Jan;45(1):460-474. doi: 10.1109/TPAMI.2022.3153645. Epub 2022 Dec 5.
Compared with short-term tracking, long-term tracking remains a challenging task that usually requires the tracking algorithm to track targets within a local region and re-detect targets over the entire image. However, few works have been done and their performances have also been limited. In this paper, we present a novel robust and real-time long-term tracking framework based on the proposed local search module and re-detection module. The local search module consists of an effective bounding box regressor to generate a series of candidate proposals and a target verifier to infer the optimal candidate with its confidence score. For local search, we design a long short-term updated scheme to improve the target verifier. The verification capability of the tracker can be improved by using several templates updated at different times. Based on the verification scores, our tracker determines whether the tracked object is present or absent and then chooses the tracking strategies of local or global search, respectively, in the next frame. For global re-detection, we develop a novel re-detection module that can estimate the target position and target size for a given base tracker. We conduct a series of experiments to demonstrate that this module can be flexibly integrated into many other tracking algorithms for long-term tracking and that it can improve long-term tracking performance effectively. Numerous experiments and discussions are conducted on several popular tracking datasets, including VOT, OxUvA, TLP, and LaSOT. The experimental results demonstrate that the proposed tracker achieves satisfactory performance with a real-time speed. Code is available at https://github.com/difhnp/ELGLT.
与短期跟踪相比，长期跟踪仍然是一项具有挑战性的任务，通常需要跟踪算法在局部区域内跟踪目标，并在整个图像上重新检测目标。然而，这方面的工作做得很少，其性能也受到限制。在本文中，我们基于所提出的局部搜索模块和重新检测模块，提出了一种新颖的鲁棒实时长期跟踪框架。局部搜索模块由一个有效的边界框回归器组成，用于生成一系列候选提议，以及一个目标验证器，用于通过其置信度分数推断最佳候选。对于局部搜索，我们设计了一种长短期更新方案来改进目标验证器。通过使用在不同时间更新的多个模板，可以提高跟踪器的验证能力。基于验证分数，我们的跟踪器确定被跟踪对象是否存在，然后在下一帧分别选择局部或全局搜索的跟踪策略。对于全局重新检测，我们开发了一种新颖的重新检测模块，该模块可以为给定的基础跟踪器估计目标位置和目标大小。我们进行了一系列实验，以证明该模块可以灵活地集成到许多其他用于长期跟踪的跟踪算法中，并且可以有效地提高长期跟踪性能。在几个流行的跟踪数据集上进行了大量实验和讨论，包括VOT、OxUvA、TLP和LaSOT。实验结果表明，所提出的跟踪器以实时速度实现了令人满意的性能。代码可在https://github.com/difhnp/ELGLT获取。
