TR2015-001

Estimating Drivable Collision-Free Space from Monocular Video


    •  Yao, J.; Ramalingam, S.; Taguchi, Y.; Miki, Y.; Urtasun, R., "Estimating Drivable Collision-Free Space from Monocular Video", IEEE Winter Conference on Applications of Computer Vision (WACV), DOI: 10.1109/WACV.2015.62, January 2015, pp. 420-427.
      BibTeX Download PDF
      • @inproceedings{Yao2015jan,
      • author = {Yao, J. and Ramalingam, S. and Taguchi, Y. and Miki, Y. and Urtasun, R.},
      • title = {Estimating Drivable Collision-Free Space from Monocular Video},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2015,
      • pages = {420--427},
      • month = jan,
      • publisher = {IEEE},
      • doi = {10.1109/WACV.2015.62},
      • url = {http://www.merl.com/publications/TR2015-001}
      • }
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  • Research Area:

    Computer Vision


In this paper we propose a novel algorithm for estimating
the drivable collision-free space for autonomous navigation
of on-road and on-water vehicles. In contrast to
previous approaches that use stereo cameras or LIDAR, we
show a method to solve this problem using a single camera.
Inspired by the success of many vision algorithms that
employ dynamic programming for efficient inference, we reduce
the free space estimation task to an inference problem
on a 1D graph, where each node represents a column in the
image and its label denotes a position that separates the free
space from the obstacles. Our algorithm exploits several
image and geometric features based on edges, color, and
homography to define potential functions on the 1D graph,
whose parameters are learned through structured SVM. We
show promising results on the challenging KITTI dataset as
well as video collected from boats.