CLUE-AD: A Context-Based Method for Labeling Unobserved Entities in Autonomous Driving Data
Abstract
Generating high-quality annotations for object detection and recognition is a challenging and important task, especially in relation to safety-critical applications such as autonomous driving (AD). Due to the difficulty of perception in challenging situations such as occlusion, degraded weather, and sensor failure, objects can go unobserved and unlabeled. In this paper, we present CLUE-AD, a general-purpose method for detecting and labeling unobserved entities by leveraging the object continuity assumption within the context of a scene. This method is dataset-agnostic, supporting any existing and future AD datasets. Using a real-world dataset representing complex urban driving scenes, we demonstrate the applicability of CLUE-AD for detecting unobserved entities and augmenting the scene data with new labels.
Cite
Text
Wickramarachchi et al. "CLUE-AD: A Context-Based Method for Labeling Unobserved Entities in Autonomous Driving Data." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.27089Markdown
[Wickramarachchi et al. "CLUE-AD: A Context-Based Method for Labeling Unobserved Entities in Autonomous Driving Data." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/wickramarachchi2023aaai-clue/) doi:10.1609/AAAI.V37I13.27089BibTeX
@inproceedings{wickramarachchi2023aaai-clue,
title = {{CLUE-AD: A Context-Based Method for Labeling Unobserved Entities in Autonomous Driving Data}},
author = {Wickramarachchi, Ruwan and Henson, Cory A. and Sheth, Amit P.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {16491-16493},
doi = {10.1609/AAAI.V37I13.27089},
url = {https://mlanthology.org/aaai/2023/wickramarachchi2023aaai-clue/}
}