De-Quan Guo, Sheng-Gui Ling, Peng Sheng, Hong-Yu Yang, L. Hong
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引用次数: 1
摘要
针对核化相关滤波器(KCF)中目标尺度变化、目标特征过于单调、跟踪误差累积等问题,提出了一种采用多特征融合的自适应KCF跟踪算法。基于位置预测、多特征融合和双线性插值对KCF跟踪算法进行了改进。其中,为了更好地表征目标的外观模型,使目标跟踪更加鲁棒,多特征融合融合了(Hue Saturation Value, HSV)颜色特征、灰度特征和改进的(Histogram of Oriented Gradient, HOG)特征。在一些目标跟踪基准上进行了定性和定量的评价,结果表明该方法的跟踪性能优于其他先进的跟踪方法。
To tackle the problem of target scale changed, too monotonous target feature, or track cumulative errors in Kernelized correlation filters(KCF), the paper proposes a self-adaptive KCF tracking algorithm employed multi-feature fusion. KCF tracking algorithm is improved based on location prediction, multi-feature fusion and bilinear interpolation. Among them, to facilitate better representation of the target's appearance model, make target tracking more robust, the multi-feature fusion is integrated (Hue Saturation Value, HSV) color features, grayscale features and improved (Histogram of Oriented Gradient, HOG) features. Both qualitative and quantitative evaluations on some object tracking benchmark show that the proposed tracking method achieves superior performance compared with other state-of-the-art methods.