To solve the problem of target loss caused by the large moving distance between two video frames during maneuvering target tracking in a video, this study proposes a kernel correlation target tracking algorithm based on one-step position prediction. Firstly, the gray scale and color feature of the target region in the current frame are extracted, and the gradient and color histogram operations of the gray scale feature and color feature are carried out respectively to obtain the FHOG feature vector and color histogram. Then, according to the color histogram, particle filter is introduced to predict the target position in the next frame. Further, the search area is determined with the predicted target position at the core. Finally, the estimated value of the current target position is corrected depending on the FHOG feature and the kernel correlation filtering in the search area. On this basis, the deformation and ambiguity generated during the movement of the maneuvering target are dealt with by the combination of the zero intercept and the average peak-to-correlation energy. Experimental results show that the proposed method can effectively improve the recognition rate in the UA-DETRAC dataset, and the success rate and accuracy of the proposed method are 11.96% and 9.6% higher than those of the standard kernel correlation filter algorithm.