In this article, phase of the Fourier transform (FT), which has observed to be a crucial component in image representation, is utilized for visual target tracking. The main aim of the proposed scheme is to reduce the computational complexity of cross-correlation-based matching frameworks. Normalized cross-correlation (NCC) function-based object tracker is converted to a phase minimization problem under the following assumption: In visual object tracking applications, if the frame rate is high, the moving object can be considered to have translational shifts in image domain in a small time window. Since the proposed tracking framework works in the Fourier domain, the translational shifts in the image space are converted to phase variations in the Fourier domain due to the “translational invariance” property of the FT. The proposed algorithm estimates the spatial target position based on the phase information of the target region. The proposed framework uses the \(\ell _1\)-norm and provides a computationally efficient solution for the tracking problem. Experimental studies indicate that the proposed phase-based technique obtain comparable results with baseline tracking algorithms which are computationally more complex.

在本文中，傅立叶变换 (FT) 的相位已被观察到是图像表示中的关键组成部分，用于视觉目标跟踪。所提出方案的主要目的是降低基于互相关的匹配框架的计算复杂度。在以下假设下，基于归一化互相关 (NCC) 函数的对象跟踪器被转换为相位最小化问题：在视觉对象跟踪应用中，如果帧速率高，则可以认为运动对象在图像域中具有平移位移在一个小的时间窗口。由于所提出的跟踪框架在傅立叶域中工作，由于 FT 的“平移不变性”特性，图像空间中的平移位移被转换为傅立叶域中的相位变化。该算法根据目标区域的相位信息估计空间目标位置。拟议的框架使用\(\ell _1\) -norm 并为跟踪问题提供了计算效率高的解决方案。实验研究表明，所提出的基于相位的技术获得了与计算上更复杂的基线跟踪算法相当的结果。