This paper proposes a new visual tracking algorithm, which leverages the merits of both template matching approaches and classification models for long-term object detection and tracking. To this end, a regression network is learned offline to detect a set of target candidates through target template matching. To cope with target appearance variations in long-term scenarios, a target-aware feature fusion mechanism is also developed, giving rise to more effective template matching. Meanwhile, a verification network is trained online to better capture target appearance and identify the target from potential candidates. During online update, contaminated training samples can be filtered out through a monitoring module, alleviating model degeneration caused by error accumulation. The regression and verification networks operate in a cascaded manner, which allows tracking to be performed in a coarse-to-fine manner and enforces the discriminative power. To further address the target reappearance issues in long-term tracking, a learning-based switching scheme is proposed, which learns to switch the tracking mode between local and global search based on the tracking results. Extensive evaluations on long-term tracking in the wild have been conducted. We achieve state-of-the-art performance on the OxUvA long-term tracking dataset. Our submission based on the proposed method has also won the 1st place of the long-term tracking challenge in VOT-2018 competition.

本文提出了一种新的视觉跟踪算法，它利用模板匹配方法和分类模型的优点进行长期目标检测和跟踪。为此，离线学习回归网络以通过目标模板匹配来检测一组目标候选。为了应对长期场景中的目标外观变化，还开发了一种目标感知特征融合机制，从而产生更有效的模板匹配。同时，在线训练验证网络以更好地捕捉目标外观并从潜在候选人中识别目标。在线更新时，可以通过监控模块过滤掉受污染的训练样本，缓解错误累积导致的模型退化。回归和验证网络以级联方式运行，这允许以粗到细的方式执行跟踪并强制执行判别力。为了进一步解决长期跟踪中的目标再现问题，提出了一种基于学习的切换方案，根据跟踪结果学习在局部搜索和全局搜索之间切换跟踪模式。已经对野外的长期跟踪进行了广泛的评估。我们在 OxUvA 长期跟踪数据集上实现了最先进的性能。我们基于所提出方法的提交还在 VOT-2018 竞赛中获得了长期跟踪挑战的第一名。它学习根据跟踪结果在本地和全局搜索之间切换跟踪模式。已经对野外的长期跟踪进行了广泛的评估。我们在 OxUvA 长期跟踪数据集上实现了最先进的性能。我们基于所提出方法的提交还在 VOT-2018 竞赛中获得了长期跟踪挑战的第一名。它学习根据跟踪结果在本地和全局搜索之间切换跟踪模式。已经对野外的长期跟踪进行了广泛的评估。我们在 OxUvA 长期跟踪数据集上实现了最先进的性能。我们基于所提出方法的提交还在 VOT-2018 竞赛中获得了长期跟踪挑战的第一名。