however, only account for the bounding box of the tracked objects. Voigtlaender  [@Voigtlaender_2019_CVPR] describe an extension of MOTA and MOTP that measure segmentation as well as tracking accuracy, proposing the sMOTSA, MOTSA and MOTSP metrics. In particular, MOTSA and MOTSP are direct equivalents of MOTA and MOTP, respectively, where the prediction-to-ground-truth matching process is formulated in terms of mask IoU instead of bounding box IoU. Finally, sMOTSA can be regarded as a “soft” version of MOTSA where the contribution of each true positive segment is weighted by its mask IoU with the corresponding ground truth segment. Please refer to Appendix \[sec:metrics\] for a formal description of these metrics.

Experimental Setup {#sec:exp-setup}
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All the results in the following sections are based on the same [MOTSNet]{}configuration, with a ResNet-50 backbone and embedding dimensionality fixed to $N_e=32$. Batches are formed by sampling a subset of $N_w$ full-resolution, contiguous frames at a random offset in one of the training sequences, for each GPU. During training we apply scale augmentation in the $[0.8,1.25]$ range and random flipping. Training by SGD follows the following linear schedule: $\text{lr}_i=\text{lr}_0(1-\frac{\#\text{steps}}{i})$, where $\text{lr}_0$ is the starting learning rate and $i$ is the training step (batch) index. Please refer to 