ScaleNet: Scale Invariant Network for Semantic Segmentation in Urban Driving Scenes

ScaleNet: Scale Invariant Network for Semantic Segmentation in Urban Driving Scenes
Mohammad Dawud Ansari, Stephan Krauß, Oliver Wasenmüller, Didier Stricker
International Conference on Computer Vision Theory and Applications (VISAPP-18), 13th, January 27-29, Funchal, Madeira, Portugal

Abstract:

The scale difference in driving scenarios is one of the essential challenges in semantic scene segmentation. Close objects cover significantly more pixels than far objects. In this paper, we address this challenge with a scale invariant architecture. Within this architecture, we explicitly estimate the depth and adapt the pooling field size accordingly. Our model is compact and can be extended easily to other research domains. Finally, the accuracy of our approach is comparable to the state-of-the-art and superior for scale problems. We evaluate on the widely used automotive dataset Cityscapes as well as a self-recorded dataset

Keywords:

Semantic Segmentation, Autonomous Driving, Labeling, Automotive, Scale