tracks are then reconstructed by applying a LAP-based algorithm, described in Sec. \[sec:inference\].

Architecture {#sec:architecture}
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[MOTSNet]{}’s architecture follows closely the implementation of Mask R-CNN described in [@Por+19_cvpr], and depicted in Figure \[fig:architecture\], with the addition of a Tracking Head (TH) which runs parallel to the Region Segmentation Head (RSH). The “backbone” of the network is composed of an FPN [@Lin2016] component on top of a ResNet-50 body [@He2015b], producing multi-scale features at five different resolutions. These features are fed to a Region Proposal Head (RPH), which predicts candidate bounding boxes where object instances could be located in the image. Instance specific features are then extracted from the FPN levels using ROI Align [@He2017] in the areas defined by the candidate boxes, and fed to the RSH and the TH. Finally, for each region, the RSH predicts a vector of class probabilities, a refined bounding box and an instance segmentation mask. Synchronized InPlace-ABN [@RotPorKon18a] is employed throughout the network after each layer with learnable parameters, except for those producing output predictions. For an in-depth description of these components we refer the reader to [@Por+19_cvpr].

#### Tracking Head (TH).

Instance segments, together with the corresponding ROI Align features from the FPN, 