Despite demonstrated success of SVM based trackers, their performance remains a boosting room if carefully considering the following factors: first, the tradeoff between sampling and budgeting samples affects tracking accuracy and efficiency much; second, how to effectively fuse different types of features to learn a robust target representation plays a key role in tracking accuracy. In this paper, we propose a novel SVM based tracking method that handles the first factor with the help of the circulant structures of the samples and the second one by a multi-kernel learning mechanism. Specifically, we formulate an SVM classification model for visual tracking that incorporates two types of kernels whose matrices are circulant, fully taking advantage of the complementary traits of the color and HOG features to learn a robust target representation. Moreover, it is fortunate that the SVM model has a closed-form solution in terms of both the classifier weights and the kernel weights, and both can be efficiently computed via fast Fourier transforms (FFTs). Extensive evaluations on OTB100 and VOT2016 visual tracking benchmarks demonstrate that the proposed method achieves a favorable performance against various state-of-the-art trackers with a speed of 50 fps on a single CPU.

中文翻译：

---

使用互补内核支持相关过滤器进行实时视觉跟踪

尽管基于SVM的跟踪器取得了成功，但如果仔细考虑以下因素，它们的性能仍然有很大的提升空间：第一，采样和预算样本之间的权衡会极大地影响跟踪的准确性和效率。其次，如何有效地融合不同类型的特征以学习鲁棒的目标表示方法，对跟踪精度起着关键作用。在本文中，我们提出了一种新颖的基于SVM的跟踪方法，该方法借助样本的循环结构来处理第一个因素，并通过多核学习机制来处理第二个因素。具体来说，我们制定了一种用于视觉跟踪的SVM分类模型，该模型结合了两种矩阵循环的内核类型，充分利用了颜色和HOG特征的互补特征来学习鲁棒的目标表示。此外，幸运的是，SVM模型在分类器权重和内核权重方面均具有封闭形式的解决方案，并且可以通过快速傅立叶变换（FFT）高效地计算两者。对OTB100和VOT2016视觉跟踪基准的广泛评估表明，该方法相对于各种最新的跟踪器在单个CPU上的速度为50 fps，具有良好的性能。