Concept for an Automatic Annotation of Automotive Radar Data Using AI-segmented Aerial Camera Images

TL;DR

This paper introduces a novel method to automatically annotate automotive radar data by using AI-segmented aerial images from UAVs, enabling quick and comprehensive labeling without occlusion issues.

Contribution

The paper proposes a new approach combining UAV aerial imagery with AI segmentation to automatically label radar data, improving annotation speed and coverage.

Findings

589 pedestrians labeled in 2 minutes

UAV images provide occlusion-free annotations

Method scalable for large datasets

Abstract

This paper presents an approach to automatically annotate automotive radar data with AI-segmented aerial camera images. For this, the images of an unmanned aerial vehicle (UAV) above a radar vehicle are panoptically segmented and mapped in the ground plane onto the radar images. The detected instances and segments in the camera image can then be applied directly as labels for the radar data. Owing to the advantageous bird's eye position, the UAV camera does not suffer from optical occlusion and is capable of creating annotations within the complete field of view of the radar. The effectiveness and scalability are demonstrated in measurements, where 589 pedestrians in the radar data were automatically labeled within 2 minutes.