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Health surveillance depends fundamentally on the reliable tracking of individual calves. Yet, prevailing multi-object tracking frameworks are prone to recurrent identification anomalies, particularly ID switches arising under occluded conditions. To overcome this limitation, we introduce YSD-BPTrack in this work, a novel multi-object tracking framework tailored for calves within occlusion-prone farm environments. The architecture is divided into two principal stages: detection and tracking. For the detection stage, DCNv4 is embedded into the YOLOv8s model to learn spatial deformations caused by occlusion, thereby strengthening detection robustness under such conditions. Meanwhile, the Star operation borrowed from StarNet is adopted to deliver high detection capability while keeping computational demands low. In the tracking stage, we first devise an original rematching mechanism (Rematching module) alongside a redesigned trajectory removal policy (Trajectory removal module). The Rematching module exploits expanded trajectory prediction boxes to re-establish correspondences with detection boxes during occlusion events, thereby cutting down on ID switch errors. The Trajectory Removal module, in turn, flexibly governs the timeout period for lost trajectories, minimizing the risk of premature trajectory deletion. Concretely, the framework we propose sets a new HOTA (Higher Order Tracking Accuracy) score of 91.6%, leading competing approaches in both tracking quality and runtime performance. Experimental evidence further demonstrates the advantage of YSD-BPTrack, with HOTA climbing by 17.6%, MOTA (Multiple Object Tracking Accuracy) by 13.9%, MOTP (Multiple Object Tracking Precision) by 1.8%, IDF1 (Identification F1 Score) by 15.4%, accompanied by reductions of 49.1% in parameters, 88.9% in IDSw (Identification Switches), and 39.2% in computational overhead relative to the other frameworks. Collectively, the presented multi-object tracking framework carries strong potential to redefine calf tracking practice.