An object tracking algorithm based on combined discriminative correlation filter and re-detection

In order to avoid tracking loss in complex scenarios with large-scale occlusion and multiple interfering factors, an object tracking algorithm based on combined discriminative correlation filter and re-detection was proposed. First, on the basis of discriminative correlation filter, a spatiotemporal regularization term was introduced and then the object scale estimation was improved, to improve the robustness of the tracker in complicated scenarios. Then, a reliability evaluation module consisting of two complementary methods was added to optimize the update strategy of the filter. Finally, a re-detection module was designed to execute two strategies for re-detection when the original tracking results are unreliable. After selecting the optimal re-detection results, the object tracking is completed. The experimental results show that, compared with the baseline tracker STRCF, the tracking precision of the proposed algorithm on OTB100 datasets and UAV123 datasets is improved by 0.9% and 4.3%, and the tracking success rate is improved by 1.5% and 5.1%. Real-time object tracking with fast running speed, good ac-curacy, high precision and strong robustness has been achieved.