In multi-object tracking tasks, the interference of external noise can lead to unreliable system modeling of traditional methods, thus reducing the accuracy of object position prediction; and the congestion and obstruction caused by dense crowds seriously affect the reliability of the object appearance, resulting in incorrect identity association. To address these issues, this study proposes a multi-object tracking algorithm Ecsort. This algorithm improves position prediction accuracy by introducing a noise compensation module based on traditional motion prediction to reduce errors caused by noise interference. Secondly, this algorithm introduces a feature similarity matching module. It can achieve accurate identity association by learning discriminative appearance features of objects and combining the advantages of motion cues and discriminative appearance features. Extensive experimental results on multi-object tracking benchmark datasets demonstrate that, compared to the baseline model, this method improves ID F1 score (IDF1), higher order tracking accuracy (HOTA), association accuracy (AssA), and detection accuracy (DetA) by 1.1%, 0.5%, 0.6%, and 0.3% respectively on the MOT17 test set, and by 2.3%, 1.9%, 3.4%, and 0.2% respectively on the MOT20 test set.