instance segmentation and tracking.

#### Deep Learning methods for MOT and MOTS.

Many deep learning methods for MOTS/VOS are based on “tracking by detection”, candidate objects are first detected in each frame, and then joined into tracklets as a post-processing step ( [@Voigtlaender_2019_CVPR]). A similar architecture, a Mask R-CNN augmented with a tracking head, is used in [@yang2019video] to tackle Video Instance Segmentation. A later work [@Luiten_2019_ICCV] improved over [@yang2019video] by adapting the UnOVOST [@ZulfikarLuitenUnOVOST] model that, first in an unsupervised setting, uses optical flow to build short tracklets later merged by a learned embedding. For the plain MOT task, Sharma  [@sharma2018beyond] incorporated shape and geometry priors into the tracklet generation process.

Other works instead, perform detection-free tracking. CAMOT [@Osep18ICRA] tracks category-agnostic mask proposals across frames, exploiting both appearance and geometry cues from scene flow and visual odometry. Finally, the VOS work in [@wang2019fast], follows a correlation-based approach, extending popular tracking models [@bertinetto2016fully; @li2018high]. Their SiamMask model only requires bounding box initialization but is pre-trained on multiple, human-annotated datasets [@LinMSCOCO2014; @xu2018youtube; @Rus+15].

Dataset Generation Pipeline {#sec:datagen}
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Our proposed data generation approach is rather generic its data source, and here we focus on the [KITTI Raw]{} [@Gei+13] dataset. [KITTI Raw]{}contains 