Abstract: In this study, an improved FairMOT model was proposed to tackle the challenges encountered in multi-object tracking of group-raised pigs, such as issues related to similar appearances, mutual occlusions, and dynamic interactions, which led to errors in identity recognition, missed detections and false detections. An EMA attention mechanism was embedded within the backbone network to optimize the feature maps obtained during the down-sampling stage and to enhance the representation of pig position features. Furthermore, a discrimination feature learning network was introduced, aiming to strengthen the fine-grained differences in appearance features among different pigs, thereby improving individual discrimination. Additionally, the model adopted a three-phase strategy for feature matching, IoU matching, and occlusion recovery matching, which enhanced the tracking accuracy. The test results in self-made datasets demonstrated that the improved FairMOT model excelled in key metrics such as HOTA, MOTA, MOTP, and IDF1, with average scores reaching 85. 87%、96. 53%、96. 07% and 94. 82% respectively. These scores significantly outperformed those of the original FairMOT model and other five trackers. The model also exhibited high accuracy and stability under various lighting and occlusion conditions, proving its effectiveness and practicality in complex environments.