This paper presents SMaRT (Stick via Motion and Recognition Tracker), a novel multi-object tracking (MOT) approach that integrates motion estimation and re-identification within a unified, efficient framework. Inspired by leading MOT methods like CenterTrack and FairMOT, SMaRT enhances tracking robustness by fusing re-identification features from an advanced teacher-student model. This integration enables the simultaneous regression of object locations and extraction of re-identification vectors within a single neural network. Evaluations on the DIVOTrack, MOT17 and SOMPT22 datasets demonstrate significant improvements over previous state-of-the-art methods in terms of Higher Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and Association Accuracy (AssA). Additionally, SMaRT’s efficiency and accuracy are validated through comprehensive synthetic video experiments, highlighting its adaptability to varied motion patterns and occlusions. The proposed approach offers a robust, accurate, and efficient solution for real-world applications such as surveillance, autonomous driving, and robotics. The tracker is available at: github.com/sompt22/SMaRT.