目的 视频跟踪中,跟踪背景复杂及目标表观变化是导致跟踪失败的主要原因.回归跟踪算法利用目标的表观信息建立回归模型进行跟踪,然而该算法的跟踪效率较低;基于循环结构的检测跟踪方法能有效利用循环结构提高跟踪效率,但该算法不能跟踪尺度变化的目标.为解决上述问题,本文提出一种基于快速傅里叶变换的尺度自适应回归跟踪算法,方法 首先利用快速傅里叶变换建立目标的核岭回归模型并搜索得到目标的中心位置,然后计算候选目标区域像素点的权重分布图,从而估计出目标的最佳尺度.结果 进行6组实验,与当前常见算法(CBWH、IVT、DFT、DSST、增量试探法)相比,本文算法不仅能较好地适应背景、目标尺度及姿态的变化,而且平均每帧运行时间短(毫秒级).结论 本文提出一种基于快速傅里叶变换的尺度自适应回归跟踪算法,算法对背景、尺度及姿态变化的目标跟踪具有较强的鲁棒性和很好的跟踪效率.

Objective Complicated tracking environments and changes in object appearance are the major causes of failure in visual tracking. The regression tracking algorithm facilitates tracking by establishing a regression model on the basis of information regarding an object's appearance. However, this algorithm displays low tracking efficiency. The use of a tracking-by-detection algorithm that is based on circular structures can improve this efficiency, but such an algorithm is unsuited for handling changes in the scale of an object. Therefore, a scale-adaptive regression tracking algorithm based on fast Fourier transform is proposed to address these problems. Method First, the algorithm determines the center position of the object in the search region using the regression model. The algorithm then estimates the ideal scale by considering the weight image of all pixels in the candidate region. Result Comparing with the popular algorithm such as CBWH、IVT and so on, the results from six experiments indicate that the proposed algorithm can not only adapt to changes in background, object scale, and pose, but that the running time of each frame is short as well.Conclusion This paper presents a scale-adaptive regression tracking algorithm based on fast Fourier transform. It owns good robustness and efficiency for tracking target with the changing of background, object's scale and pose.