核相关滤波算法的出现将传统目标跟踪算法推向了一个新的高度,以其高速度、高精度、高鲁棒性的特点迅速获得了广泛认可与关注。针对核相关滤波算法在尺度变化和快速移动方面的不足,基于目标在发生尺度变化时其主要特征的相对位置可以反映其尺度的大小,本文采用分块检测的方式来进行目标的尺度计算;在特征模板更新方面,针对核相关滤波算法处理高速变化目标容易丢失的问题,采用卡尔曼滤波预测的方式提前更新目标模板,以解决模板更新滞后的问题。实验数据表明,本文算法能够稳定地提高跟踪精度。

The emergence of kernelized correlation filter has pushed the traditional target tracking algorithm to a new height. With its high speed, high precision, and high robustness, it has quickly gained wide recognition and attention from the society. In view of the shortcomings of the kernelized correlation filter in scale change, based on the relative position of the main features of the target when the scale changes and fast moving, the size of the target can be reflected. The block detection method is used to calculate the scale of the target. In respect, the kernelized correlation filter loses the target when dealing with the target with high-speed changes of the feature, and the target template is updated in advance by Kalman filter prediction to solve the problem of template update lag. Experimental results show that the proposed algorithm can improve the tracking accuracy steadily.