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Multi-object tracking and segmentation (MOTS) is a critical task forautonomous driving applications. The existing MOTS studies face two criticalchallenges: 1) the published datasets inadequately capture the real-worldcomplexity for network training to address various driving settings; 2) theworking pipeline annotation tool is under-studied in the literature to improvethe quality of MOTS learning examples. In this work, we introduce theDG-Labeler and DGL-MOTS dataset to facilitate the training data annotation forthe MOTS task and accordingly improve network training accuracy and efficiency.DG-Labeler uses the novel Depth-Granularity Module to depict the instancespatial relations and produce fine-grained instance masks. Annotated byDG-Labeler, our DGL-MOTS dataset exceeds the prior effort (i.e., KITTI MOTS andBDD100K) in data diversity, annotation quality, and temporal representations.Results on extensive cross-dataset evaluations indicate significant performanceimprovements for several state-of-the-art methods trained on our DGL-MOTSdataset. We believe our DGL-MOTS Dataset and DG-Labeler hold the valuablepotential to boost the visual perception of future transportation.

DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception