FairMOT, a multi-object tracking algorithm, proposes a balanced learning strategy between the detection branch and the re-identification branch, effectively balancing the tasks of object detection and re-identification, thereby improving tracking accuracy. However, due to the limited feature extraction capability of its DLA34 backbone network, the model′s tracking performance often declines in complex real-world scenarios, leading to missed detections and incorrect tracking. To enhance the backbone network′s feature extraction capability, this paper designs a deep aggregated backbone network based on an element-wise multiplication structure and proposes the FairMOT-Star algorithm. This algorithm leverages the principle of hidden dimension enhancement brought by the element-wise multiplication structure to achieve concise and efficient object feature extraction. Additionally, EIoU_Loss is used as the regression loss function for the bounding box regression task, more precisely describing the positional and shape relationships between detection boxes and ground truth boxes, thus improving prediction accuracy. In the matching and association part, the Kalman filter algorithm predicts target motion information, and the Hungarian algorithm associates and matches targets and trajectories across frames in the temporal dimension. Experimental tests on the MOT16 dataset achieved an MOTA accuracy of 86.0%. The model′s weight parameters amount to 19.59 M, reducing parameter count by 9.7% compared to the FairMOT model, while increasing MOTA accuracy by 3.5%, effectively optimizing the computational parameters and tracking accuracy of the FairMOT algorithm.