基于傅里叶域卷积表示的目标跟踪算法

针对目标跟踪问题，提出基于傅里叶域卷积表示的目标跟踪算法，将目标跟踪问题转化为卷积表示模型，通过求解最优滤波器，得到对目标函数的最佳表示，可以实现快速鲁棒的跟踪。多通道卷积表示模型在傅里叶域等价于求解线性方程的最佳近似解。首先，通过广义逆理论求得该方程的最优通解，给出一般滤波器的表示形式；然后，利用前一时刻的滤波器和当前特征模板生成当前滤波器，利用满秩算法快速求解广义逆；最后，在位移和尺度上更新、应用该滤波器。在目标跟踪基准（OTB）数据库中的大量实验表明，本文算法比当前部分较为先进的跟踪算法具有更好的表现，并提供了更加灵活多样的滤波器设计。

Convolution representation-based object tracking algorithm in Fourier domain

A novel object tracking algorithm based on convolution representation in Fourier domain is pro-posed for object tracking.Object tracking question can be treated as a convolution representation model.By finding the best filters, which reconstruct the target function with minimum loss, fast and robust object tracking can be realized.When the optimal multi-channel convolution representation model is mapped to the Fourier domain, it is equal to solving the least squares solution to linear equations.First, all solutions of the system of linear equations can be expressed through the theory of pseudo inverse, which provide a general format of con-volution filters.Then, filters updated in the previous frame and feature templates extracted from current frame are used to generate current filters, and the pseudo inverse can be obtained fast through the full rank algo-rithm.Finally, tracking filters are updated and applied in both translation and scale.Experimental results on the object tracking benchmark ( OTB) database show that our algorithm performs better than some state-of-the-art tracking methods in terms of accuracy and offers a general format to design filters.