Abstract: In the field of object tracking, the discriminant method based on correlation filter theory has made a series of advances and becomes a hot research topic because of its efficiency and robustness. The current research statuses of the tracking field were reviewed to allow more scholars to explore the theory and development of correlation filter-based trackers. First, the correlation filter theory and the general framework for object tracking were introduced, and the classical kernelized correlation filter was described in detail. Second, the difficulties of object tracking technology when applied in the real application were discussed, and the main difficulties of feature representation and adaptive scale updating were analyzed in detail. Then, the representative algorithms were analyzed and discussed from the four categories of basic correlation filter, part correlation filter, regularized correlation filter, and Siamese network correlation filter, and the possible future development trend was pointed out. Finally, 32 types of correlation filter-based trackers were compared in terms of accuracy, success rate and frame rate on the OTB2013 and OTB100 standard data sets, and 10 types of correlation filter-based trackers were compared in terms of EAO, and the accuracy and robustness on the VOT2017 data set, further indicated the advantages of correlation filter tracking algorithms. The research on correlation filter theory has extensive applications in the object tracking field. However, it is still a challenging research direction due to the influence of complex scenes and their own factors. Developing a highly efficient and robust correlation filter tracker is considered significant.