Classification and regression-based trackers (CAR) are widely adopted to tackle the short-term visual tracking task. However, the existing CAR tackers either employ offline-trained regression models based on predefined anchor-boxes, or online update their models in a rough and inflexible way, which leads to the lack of long-term adaptability for target deformations and appearance variations. To overcome this limitation, we propose a novel long-term tracking framework LT-CAR utilizing sample-efficient meta learning to online optimize both the classification and regression model. Specifically, we first introduce the ridge regression to a fully convolutional network as our regression branch, and then implement a vertically stacked GRU module termed as Meta-Sample-Filter to keep historical information about the target as well as help our model learn what to learn. Moreover, we extend our framework for long-term tracking by introducing a carefully designed spatial–temporal verification network to identify tracking failures, and a query-guided detector to conduct global re-detection. Experimental results on LaSOT, VOT-LT2018, VOT-LT2019, and TLP benchmarks show that our LT-CAR achieves comparable performance to the state-of-the-art long-term algorithms.

基于分类和回归的跟踪器（CAR）被广泛用于解决短期视觉跟踪任务。但是，现有的CAR跟踪器要么基于预定义的锚框采用离线训练的回归模型，要么以粗糙且不灵活的方式在线更新其模型，这导致缺乏对目标变形和外观变化的长期适应性。为了克服此限制，我们提出了一种新颖的长期跟踪框架LT-CAR，该框架利用样本有效的元学习在线优化分类和回归模型。具体来说，我们首先将岭回归引入全卷积网络作为我们的回归分支，然后实施称为Meta-Sample-Filter的垂直堆叠GRU模块，以保留有关目标的历史信息，并帮助我们的模型学习所学内容。此外，我们通过引入精心设计的时空验证网络来识别跟踪故障，以及通过查询引导的检测器来进行全局重新检测，扩展了用于长期跟踪的框架。在LaSOT，VOT-LT2018，VOT-LT2019和TLP基准测试中的实验结果表明，我们的LT-CAR具有与最新的长期算法相当的性能。