针对核相关滤波算法中单一特征不能很好地适应跟踪过程中出现的复杂场景,以及算法无法解决目标尺度变化的问题,提出一种多特征融合的尺度自适应相关滤波跟踪算法。首先,在相关滤波算法的框架下,按照特征响应图的可信度来对快速方向梯度直方图 (FHOG)特征和局部二值模式(LBP)特征进行自适应加权融合,实现对目标的定位;其次,在尺度估计环节,利用尺度金字塔来估计目标的尺度大小,使算法对尺度发生变化的目标有很好的适应能力;最后,在OTB-50数据集上进行测试,将本文算法与其他5种跟踪方法进行对比,其精确率和成功率均有所提高,且具有较好的鲁棒性和稳定的跟踪性能。

In this study, we propose a scale-adaptive correlation filter tracking algorithm based on the fusion of multiple features to handle the problems that the single feature of the kernel correlation filtering algorithm cannot adapt to the complex scenes observed during the tracking process and that the kernel correlation filtering algorithm cannot handle the scale changes of the target. First, under the framework of the correlation filtering algorithm, the fast histogram of oriented gradient and local binary pattern features are weighted adaptively based on the reliability of the feature response graph for localizing the target. Second, the scale estimation process estimates the scale of target using a scale pyramid to ensure good adaptability with respect to the target with scale change. The proposed algorithm and five other tracking methods are verified by testing on the OTB-50 dataset. Apart from outperforming the existing methods in terms of the accuracy and success rates, the proposed algorithm exhibits good robustness and a stable tracking performance.