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  4. Foveal vision for instance segmentation of road images
 
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2018
  • Konferenzbeitrag

Titel

Foveal vision for instance segmentation of road images

Abstract
Instance segmentation is an important task for the interpretation of images in the area of autonomous or assisted driving applications. Not only indicating the semantic class for each pixel of an image, but also separating different instances of the same class, even if neighboring in the image, it can replace a multi-class object detector. In addition, it offers a better localization of objects in the image by replacing the object detector bounding box with a fine-grained object shape. The recently presented Cityscapes dataset promoted this topic by offering a large set of data labeled at pixel level. Building on the previous work of (Uhrig et al., 2016), this work proposes two improvements compared to this baseline strategy leading to significant performance improvements. First, a better distance measure for angular differences, which is unaffected by the -p/p discontinuity, is proposed. This leads to improved object center localization. Second, the imagery from vehicle perspective includes a fixed vanishing point. A foveal concept counteracts the fact that objects get smaller in the image towards this point. This strategy especially improves the results for small objects in large distances from the vehicle.
Author(s)
Ortelt, B.
Herrmann, C.
Willersinn, Dieter
Beyerer, Jürgen
Hauptwerk
VISIGRAPP 2018, 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Proceedings. Vol.4: VISAPP
Konferenz
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) 2018
International Conference on Computer Vision Theory and Applications (VISAPP) 2018
DOI
10.5220/0006616103710378
File(s)
N-510040.pdf (5.46 MB)
Language
Englisch
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