A dairy goat tracking method via lightweight fusion and Kullback Leibler divergence

As an important task in precision breeding, accurately tracking dairy goats is essential for their disease prediction and abnormal behavior recognition. For efficiently capturing local features in Transformer and reducing the noise in the template of dairy goat, we develop an innovative dairy goat tracking algorithm that introduces an asymmetric fusion architecture and Kullback Leibler loss to improve accuracy and generate a low-noise template. Based on TrDiMP, we construct a fusion mechanism that embeds high-dimensional timing information into its evaluation branch to enhance discrimination ability. Then, we exploit an asymmetric attention mechanism to incorporate convolutional features of different sizes and raise the percentage of local linear information in smaller features. Finally, we incorporate Kullback Leibler divergence to train the loss between current frames and the goat template, guiding the template to conform to the probability density distribution of dairy goats and reducing noise. The AUC (Area Under Curve) and Precision of the improved TrDiMP reach 77.56% and 66.10%, respectively, which are 2.91% and 4.55% higher than that of TrDiMP. The average tracking speed of the improved TrDiMP is 14.90 FPS (frames per second), 0.36 FPS faster than that of TrDiMP. The overall results demonstrate that our model can track dairy goats and provide an effective approach to capture their dynamic movement in a real pasture.

Automated summary

Accurately tracking dairy goats is crucial for disease prediction and abnormal behavior recognition. We developed an innovative tracking algorithm that utilizes an asymmetric fusion architecture and Kullback Leibler loss to improve accuracy and reduce noise. By incorporating high-dimensional timing information and an asymmetric attention mechanism, our model achieved significant performance improvement.