Abstract: In recent years, due to the advantages of fast speed and strong robustness, correlation filter based methods have been developed rapidly in the tracking community. However, when the existing models are used to deal with complex scenes, it is difficult to meet the requirements of practical application. The background aware correlation filter (BACF) suffers from the maximum response weakening problem when handling the challenging scenes, such as rotation of the target appearance, scale variation and out of view, thus result in inaccurate tracking result. In order to tackle these problems, a target redetection method for visual tracking based on correlation filter was proposed. On the basis of the background aware correlation filter, a correlation response detection mechanism was introduced to judge the quality of the tracking result generated by the correlation filter. After detecting the tracking result was not credible, a particle filter resampling strategy was exploited to generate abundant particles which was beneficial to perceive the state of the target, and the center of the target could be redetected. On this foundation, an adaptive scale estimation mechanism was adopted to calculate the size information for the target, by which the final tracking result could be obtained. To validate the effectiveness of the improved algorithm, the extensive experiments on three public datasets: OTB2013, OTB2015 and VOT2016 were conducted, meanwhile, several state-of-the-art trackers: correlation filter and deep learning based trackers were also chosen as comparison, and the performance of all the compared trackers was shown from the aspects of annotated video attributes, tracking accuracy, and robustness of the algorithms. Experimental results demonstrate that the proposed target redetection tracker achieve a favorable performance on these three datasets, meanwhile, it effectively improves the accuracy and success rate of the BACF when handling the challenging situations of target rotation, scale variation, and out of view.