Copyright 2020 - TUE - Mobile Perception Systems

The data sets as discussed in my ITSC 2014, ITSC 2015 and EI 2017 publications are available via the request form below. They consist of 74, 114, and 265 RGB Stereo camera sequences, respectively, recorded from within a car moving through everyday traffic in and around Eindhoven (the Netherlands). The last frame of each sequence comes with an annotated ground truth of the road area. The figure shows downsized examples.

You can contact W.P. Sanberg (w.p.sanberg 'at' tue.nl) if you are curious or have any questions regarding this work.

 

 

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License / Citation

Creative Commons LicenseEHV-road 14, 15 and 17 W.P. Sanberg are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

 

This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. Contact me to discuss permissions beyond the scope of this license.

 

If you use this data, please cite the corresponding paper:
@INPROCEEDINGS{Sanberg2014ITSC,  
  author = {Willem P. Sanberg and G. Dubbelman and Peter H.N. de With},
  title = {Extending the Stixel World with Online Self-Supervised Color Modeling for Road-Versus-Obstacle Segmentation},  
  booktitle = {IEEE Int. Conf. on Intelligent Transportation Systems (ITSC)},  
  pages = {1400--1407},  
  year = {2014},  
  doi = {10.1109/ITSC.2014.6957883} }

@INPROCEEDINGS{Sanberg2015ITSC,  
  author = {Willem P. Sanberg and G. Dubbelman and Peter H.N. de With},
  title = {Color-based Free-Space Segmentation using Online Disparity-supervised Learning},  
  booktitle = {IEEE Int. Conf. on Intelligent Transportation Systems (ITSC)},  
  pages = {906--912},  
  year = {2015},  
  doi = {10.1109/ITSC.2015.152} }

 

@INPROCEEDINGS{Sanberg2017EI,
author = {Willem P. Sanberg and G. Dubbelman and Peter H.N. de With},
title = {Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks},
booktitle = {IS&T Electronic Imaging - Autonomous Vehicles and Machines},
pages = (54--61),
year = {2017},
doi = {10.2352/ISSN.2470-1173.2017.19.AVM-021}}

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