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. 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. 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.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.