\hat s\right\}\,,$$ which contains all terminal (most recent) segments of the tracklets seen in the past $N_w$ frames. As a final step after processing all frames in the sequence, we discard the tracklets with fewer than $N_t$ segments, as, empirically, very short tracks usually arise from spurious detections.

Experiments {#sec:experiments}
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We provide a broad experimental evaluation assessing i) the quality of our automatically harvested [KITTI Synth]{}dataset on the MOTS task by evaluating it against the [KITTI MOTS]{}dataset, ii) demonstrate the effectiveness of [MOTSNet]{}and our proposed mask-pooling layer against strong baselines on the [KITTI MOTS]{}and MOTSChallenge datasets, iii) demonstrate the generality of our MOTS label generation process by extending the BDD100k tracking dataset with segmentation masks to become a MOTS variant thereof and iv) provide an ablation about the contribution of association terms for the final tracklet extraction. While we can benchmark the full MOTS performance on [KITTI MOTS]{}and MOTSChallenge, we are able to infer the tracking performance based on the ground truth box-based tracking annotations available for BDD100k.

Evaluation Measures
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The CLEAR MOT [@bernardin2008evaluating] metrics, including Multi-Object Tracking Accuracy (MOTA) and Precision (MOTP), are well established as a standard set of measures for evaluating Multi-Object Tracking systems. These, 