Detection of Collective Anomalies in Images for Automated Driving Using an Earth Mover’s Deviation (EMDEV) Measure

Abstract

For visual perception in automated driving, a reliable detection of so-called corner cases is important. Corner cases appear in many different forms and can be image frame- or sequence-related. In this work, we consider a specific type of corner case: collective anomalies. These are instances that appear in unusually large amounts in an image. We propose a detection method for collective anomalies based on a comparison of a test (sub-)set instance distribution to a training (i.e., reference) instance distribution, both distributions obtained by an instance-based semantic segmentation. For this comparison, we propose a novel so-called earth mover’s deviation (EMDEV) measure, which is able to provide signed deviations of instance distributions. Further, we propose a sliding window approach to allow the comparison of instance distributions in an online application in the vehicle. With our approach, we are able to identify collective anomalies by the proposed EMDEV measure, and to detect deviations from the instance distribution of the reference dataset.