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.

中文翻译：

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具有自适应提议选择的相关滤波器跟踪用于准确的尺度估计

最近，一些带有检测提议的基于相关滤波器的跟踪器已经取得了最先进的跟踪结果。然而，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提议选择算法，该算法可以生成少量高质量提议来处理视觉对象跟踪的尺度变化问题。具体来说，我们首先利用HSV颜色空间中的颜色直方图来表示实例（即第一帧中的初始目标和前一帧中的预测目标）和建议。然后，制定基于颜色相似度的自适应策略来选择高质量的建议。我们进一步将所提出的自适应建议选择算法与从粗到细的深度特征相结合，以验证所提出的跟踪器的泛化性和效率。在两个基准数据集上的实验表明，所提出的算法在对抗几个最先进的跟踪器时表现良好。