Fig 2  Field Enhancement Refinement.
Focus-MOT has four loss functions, which correspond to the loss hm_loss for heatmap, wh_loss for boxsize, off_loss for offset, and id_loss for Re-ID. for hm_loss, we use the MSE loss function to calculate; for wh_loss, we use the L1 loss function to calculate; for off_loss, we use the multivariate cross-entropy function to calculate. use the L1 loss loss function to calculate; for off_loss, we use the L1 loss function to calculate, and for Re-ID loss we use the multivariate cross entropy function to calculate.
Then the total loss is:
\begin{equation} \text{loss}\ =\ \text{hm}\_\text{loss}\ +\ \text{wh}\_\text{loss}\ +\ \text{off}\_\text{loss}\ +\ 0.1\times\text{id}\_\text{loss}\nonumber \\ \end{equation}
Focus-MOT uses the training set provided by the six datasets MOT17, Caltech, Citypersons, Cuhksysu, PRW, and ETH, and the test set of MOT15 and MOT17 is used for testing [8-12].