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Pedestrian Detection using Stereo-vision and Graph Kernels
Frederic Suard, Vincent Guigue, Alain Rakotomamonjy and Abdelaziz Bensrhair
In: IEEE Symposium on Intelligent Vehicule, Las Vegas(2005).
AbstractThis paper presents a method for pedestrian detection
with stereovision and graph comparison. Images are
segmented thanks to the NCut method applied on a single image,
and the disparity is computed from a pair of images. This segmentation
enables us to keep only shapes of potential obstacles, by
eliminating the background. The comparison between two graphs
is accomplished with a inner product for graph, and then the
recognition stage is performed learning is done among several
pedestrian and non-pedestrian graphs with SVM method. The
results that are depicted are preliminary results but they show
that this approach is very promising since it clearly demonstrates
that our graph representation is able to deal with the variability
of pedestrian pose. | EPrint Type: | Conference or Workshop Item (Poster) |
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| Project Keyword: | Project Keyword UNSPECIFIED |
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| Subjects: | Machine Vision |
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| ID Code: | 1388 |
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| Deposited By: | Alain Rakotomamonjy |
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| Deposited On: | 28 November 2005 |
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