A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
Maria Lyssenko Christoph Gladisch Christian Heinzemann Matthias Woehrle Rudolph Triebel
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.