  Multi-object tracking and segmentation (MOTS) is a critical task for
autonomous driving applications. The existing MOTS studies face two critical
challenges: 1) the published datasets inadequately capture the real-world
complexity for network training to address various driving settings; 2) the
working pipeline annotation tool is under-studied in the literature to improve
the quality of MOTS learning examples. In this work, we introduce the
DG-Labeler and DGL-MOTS dataset to facilitate the training data annotation for
the MOTS task and accordingly improve network training accuracy and efficiency.
DG-Labeler uses the novel Depth-Granularity Module to depict the instance
spatial relations and produce fine-grained instance masks. Annotated by
DG-Labeler, our DGL-MOTS dataset exceeds the prior effort (i.e., KITTI MOTS and
BDD100K) in data diversity, annotation quality, and temporal representations.
Results on extensive cross-dataset evaluations indicate significant performance
improvements for several state-of-the-art methods trained on our DGL-MOTS
dataset. We believe our DGL-MOTS Dataset and DG-Labeler hold the valuable
potential to boost the visual perception of future transportation.
