论文标题
相关滤波器跟踪与自适应提案选择，以进行准确的比例估计
Correlation filter tracking with adaptive proposal selection for accurate scale estimation
论文作者
论文摘要
最近，一些具有检测建议的基于相关滤波器的跟踪器已达到最新的跟踪结果。但是，提案生成器给出的大量冗余提案可能会降低这些跟踪器的性能和速度。在本文中，我们提出了一种自适应建议选择算法，该算法可以生成少量的高质量建议来处理视觉对象跟踪的规模变化问题。具体而言，我们首先利用HSV颜色空间中的颜色直方图来表示实例（即，在第一帧中的初始目标和上一个帧中的预测目标）和提案。然后，制定基于颜色相似性的自适应策略以选择高质量的建议。我们进一步将提出的自适应建议选择算法与粗到细的深度特征整合在一起，以验证所提出的跟踪器的概括和效率。两个基准数据集上的实验表明，所提出的算法对几个最新的跟踪器的性能有利。
Recently, some correlation filter based trackers with detection proposals have achieved state-of-the-art tracking results. However, a large number of redundant proposals given by the proposal generator may degrade the performance and speed of these trackers. In this paper, we propose an adaptive proposal selection algorithm which can generate a small number of high-quality proposals to handle the problem of scale variations for visual object tracking. Specifically, we firstly utilize the color histograms in the HSV color space to represent the instances (i.e., the initial target in the first frame and the predicted target in the previous frame) and proposals. Then, an adaptive strategy based on the color similarity is formulated to select high-quality proposals. We further integrate the proposed adaptive proposal selection algorithm with coarse-to-fine deep features to validate the generalization and efficiency of the proposed tracker. Experiments on two benchmark datasets demonstrate that the proposed algorithm performs favorably against several state-of-the-art trackers.
