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arXiv · 2110.07790

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

Yiming Cui·Zhiwen Cao·Yixin Xie·Xingyu Jiang·Feng Tao·Yingjie Chen·Lin Li·Dongfang Liu
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Abstract

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.

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Yiming Cui, Zhiwen Cao, Yixin Xie, Xingyu Jiang, Feng Tao, Yingjie Chen, Lin Li, Dongfang Liu. 2021-10-15. DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception. https://arxiv.org/abs/2110.07790

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