A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
<|reference_start|>A Flow-based Credibility Metric for Safety-critical Pedestrian Detection: Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes utilize safety-agnostic metrics to argue sufficient detection performance. Hence, it is imperative to leverage supplementary domain knowledge to accentuate safety-critical misdetections during evaluation tasks. To tackle the underspecification, this paper introduces a novel credibility metric, called c-flow, for pedestrian bounding boxes. To this end, c-flow relies on a complementary optical flow signal from image sequences and enhances the analyses of safety-critical misdetections without requiring additional labels. We implement and evaluate c-flow with a state-of-the-art pedestrian detector on a large AD dataset. Our analysis demonstrates that c-flow allows developers to identify safety-critical misdetections.<|reference_end|>
@article{lyssenko2024a,
    title={A Flow-based Credibility Metric for Safety-critical Pedestrian Detection},
    author={Maria Lyssenko, Christoph Gladisch, Christian Heinzemann, Matthias
  Woehrle, Rudolph Triebel},
    journal={arXiv preprint arXiv:2402.07642},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07642},
    primaryClass={cs.CV cs.LG}
}