Long-term occlusion is one of the most formidable challenges in Multi-Object Tracking (MOT). The motion models of existing SORT-based trackers are unreliable in estimating the motion states of long-term occluded targets. This is mainly because as the occlusion period increases, the increases speed of estimation errors in the motion model increases faster. In practical applications, we believe that the estimation error of the tracker during long-term occlusion is mainly concentrated in the estimation error of the motion model on the velocity of the occluded target. In this work, we have demonstrated that in the long-term occlusion period, appropriately correcting the estimated values of the motion model on the target motion velocity and fully utilizing the temporal and attribute information of the target’s historical trajectory as calculation indicators of correlation are beneficial for improving the robustness of the tracker in long-term occlusion. We refer to our proposed motion correction-based framework as MC-SORT, which mainly consists of a Momentum Compensation Module (MCM) and a Backtracking Re-association (BRA) module. The former can correct the estimated value of the target’s motion state during long-term occlusion, the latter uses the temporal and attribute information of the target’s historical trajectory during long-term occlusion as correlation indicators to measure the degree of correlation between the target and trajectory. Our proposed MC-SORT has the characteristics of simplicity, online, real-time, and plug-and-play, particularly improving the robustness of the tracker in long-term occlusion. The extensive experimental results on the MOT17 and MOT20 datasets demonstrate the robustness and superiority of our framework.