How Machine Perception Relates to Human Perception: Visual Saliency and Distance in a Frame-by-Frame Semantic Segmentation Task for Highly/Fully Automated Driving

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.