Foveal Vision for Instance Segmentation of Road Images

Benedikt Ortelt, Christian Herrmann, Dieter Willersinn, Jürgen Beyerer

Abstract

Instance-based semantic labeling 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 from 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 on pixel level. Building on the previous work of \cite{uhrig2016b}, 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 $-\pi/\pi$ 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.

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Paper Citation


in Harvard Style

Ortelt B., Herrmann C., Willersinn D. and Beyerer J. (2018). Foveal Vision for Instance Segmentation of Road Images.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-290-5, pages 371-378. DOI: 10.5220/0006616103710378


in Bibtex Style

@conference{visapp18,
author={Benedikt Ortelt and Christian Herrmann and Dieter Willersinn and Jürgen Beyerer},
title={Foveal Vision for Instance Segmentation of Road Images},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2018},
pages={371-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006616103710378},
isbn={978-989-758-290-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Foveal Vision for Instance Segmentation of Road Images
SN - 978-989-758-290-5
AU - Ortelt B.
AU - Herrmann C.
AU - Willersinn D.
AU - Beyerer J.
PY - 2018
SP - 371
EP - 378
DO - 10.5220/0006616103710378