High quality object proposals are crucial in visual tracking algorithms that utilize region proposal network (RPN). Refinement of these proposals, typically by box regression and classification in parallel, has been popularly adopted to boost tracking performance. However, it still meets problems when dealing with complex and dynamic background. Thus motivated, in this paper we introduce an improved proposal refinement module, Cascaded Regression-Align-Classification (CRAC), which yields new state-of-the-art performances on many benchmarks. First, having observed that the offsets from box regression can serve as guidance for proposal feature refinement, we design CRAC as a cascade of box regression, feature alignment and box classification. The key is to bridge box regression and classification via an alignment step, which leads to more accurate features for proposal classification with improved robustness. To address the variation in object appearance, we introduce an identification-discrimination component for box classification, which leverages offline reliable fine-grained template and online rich background information to distinguish the target from background. Moreover, we present pyramid RoIAlign that benefits CRAC by exploiting both the local and global cues of proposals. During inference, tracking proceeds by ranking all refined proposals and selecting the best one. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors and runs in real-time.

翻译：高质量的目标提案对于利用区域提案网络(RPN)进行视觉跟踪算法至关重要。改进这些提案,通常采用盒式回归和平行分类,被广泛采用,以提高跟踪业绩。然而,在处理复杂和动态背景时,仍然遇到问题。因此,我们在本文件中引入了改进提案改进模块,即累进回归-质量分类(CRAC),该模块在许多基准上产生新的最新最新最佳业绩。首先,注意到从箱式回归中获得的抵消可以作为改进提案特征的指导,我们设计了CRAC,作为箱式回归、特征调整和箱分类的连锁。关键在于通过调整步骤连接框式回归和分类,使建议分类具有更加稳健的更准确性。为了应对目标外观的变化,我们引入了箱分类识别歧视部分,利用离线性精细模板和在线丰富的背景资料来区分目标。此外,我们展示了金字塔,通过利用地方和20级的框式回归、特征调整和框式组合分类,通过升级的列表,包括不断升级的OASTBA、S、S、S、S、S、S、S、AV、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、A、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S、S