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An improved KCF tracking algorithm based on multi-feature and multi-scale

机译：一种基于多特征多尺度的改进的KCF跟踪算法

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The purpose of visual tracking is to associate the target object in a continuous video frame. In recent years, the method based on the kernel correlation filter has become the research hotspot. However, the algorithm still has some problems such as video capture equipment fast jitter, tracking scale transformation. In order to improve the ability of scale transformation and feature description, this paper has carried an innovative algorithm based on the multi feature fusion and multi-scale transform. The experimental results show that our method solves the problem that the target model update when is blocked or its scale transforms. The accuracy of the evaluation (OPE) is 77.0%, 75.4% and the success rate is 69.7%, 66.4% on the VOT and OTB datasets. Compared with the optimal one of the existing target-based tracking algorithms, the accuracy of the algorithm is improved by 6.7% and 6.3% respectively. The success rates are improved by 13.7% and 14.2% respectively.
机译：视觉跟踪的目的是将目标对象关联到一个连续的视频帧中。近年来，基于核相关滤波器的方法已成为研究热点。但是，该算法仍然存在一些问题，例如视频捕获设备的快速抖动，跟踪尺度转换。为了提高尺度变换和特征描述的能力，本文提出了一种基于多特征融合和多尺度变换的创新算法。实验结果表明，该方法解决了目标模型受阻或比例变换时更新的问题。在VOT和OTB数据集上，评估（OPE）的准确性为77.0％，75.4％，成功率为69.7％，66.4％。与现有的基于目标的最优跟踪算法相比，该算法的精度分别提高了6.7％和6.3％。成功率分别提高了13.7％和14.2％。

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