Fig. 5: Comparison of confusion matrices for behaviour classification using SlowFast and TimeSformer architectures under different training conditions.
From: Video-based cattle behaviour detection for digital twin development in precision dairy systems
A SlowFast model trained on the unaugmented dataset. B SlowFast model trained on the augmented dataset. C TimeSformer model trained on the unaugmented dataset. D TimeSformer model trained on the augmented dataset. Each panel shows the normalized confusion matrix for classification across the seven behavioural categories. Rows represent ground-truth labels and columns represent predicted labels. The matrices illustrate class-wise prediction accuracy and misclassification patterns, enabling comparison of architectural performance and the influence of data augmentation on behavioural separability.
