A Real-Time dairy goat tracking based on MixFormer with adaptive token elimination and efficient appearance update

Intelligent tracking and monitoring are fundamental to achieving precise posture recognition, health assessment, and accurate breeding in dairy goats. In view of the motion characteristics of dairy goats, which often exhibit significant posture changes due to their lively and active movements, we present an object tracking method for a single dairy goat based on MixFormer optimized through adaptive token removal and efficient appearance update. Firstly, we collected motion data from two different breeding farms and trained the model solely on one farm and tested it in the other to verify its generalization performance. Then, through an early Candidate Elimination (CE) module, we pre-filter redundant background and similar object information in dairy goats, thereby reducing computational load and enhancing tracking speed. Finally, we introduce the adaptive appearance update strategy named Average Peak-to-Correlation Energy (APCE) to maximize the utilization of target-related historical information, thus improving target localization accuracy. The experimental results indicate that the improved MixFormer achieves 75.79% and 74.28% in terms of AUC (Area Under Curve) and Precision, respectively. These results represent a 2.75% and 7.38% enhancement over the MixFormer, outperforming KYS, KeepTrack, STARK and TOMP. The improved MixFormer runs at 30.5 fps, meeting real-time tracking requirements. This study demonstrates that the proposed approach offers an effective solution for automatic tracking and monitoring of dairy goats in complex breeding conditions.