DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception
This addresses data quality and annotation challenges for autonomous driving perception, but it is incremental as it builds on existing datasets like KITTI MOTS and BDD100K.
They tackled the lack of high-quality datasets and annotation tools for multi-object tracking and segmentation in autonomous driving by introducing DG-Labeler and the DGL-MOTS dataset, which improved training accuracy and efficiency, leading to significant performance gains in cross-dataset evaluations.
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.