Publikation
How Machine Perception Relates to Human Perception: Visual Saliency and Distance in a Frame-by-Frame Semantic Segmentation Task for Highly/Fully Automated Driving
Nico Herbig; Frederik Wiehr; Atanas Poibrenski; Janis Sprenger; Christian Müller
In: ACM/IEEE 1st International Workshop on Software Engineering for AI in Autonomous Systems. International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS-2018), located at ICSE, May 27 - June 3, Gothenburg, Sweden, ISBN 978-1-4503-5739-5/18/05, ACM/IEEE, 2018.
Zusammenfassung
In this paper, we investigate the link between machine perception and human perception for highly/fully automated driving. We compare the classification results of a camera-based frame-by-frame semantic segmentation model (Machine) with a well-established visual saliency model (Human) on the Cityscapes dataset. The results show that Machine classifies foreground objects better if they are more salient, indicating a similarity with the human visual system. For background objects, the accuracy drops when the saliency increases, giving evidence for the assumption that Machine has an implicit concept of saliency.