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Scene Flow Estimation from Light Fields via the Preconditioned Primal-Dual Algorithm Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Object-Level Priors for Stixel Generation

Authors : Marius Cordts, Lukas Schneider, Markus Enzweiler, Uwe Franke, Stefan Roth

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

This paper presents a stereo vision-based scene model for traffic scenarios. Our approach effectively couples bottom-up image segmentation with object-level knowledge in a sound probabilistic fashion. The relevant scene structure, i.e. obstacles and freespace, is encoded using individual Stixels as building blocks that are computed bottom-up from dense disparity images. We present a principled way to additionally integrate top-down prior information about object location and shape that arises from independent system modules, ranging from geometric cues up to highly confident object detections. This results in an efficient exploration of orthogonal image-based cues, such as disparity and gray-level intensity data, combined in a consistent scene representation. The overall segmentation problem is modeled as a Markov Random Field and solved efficiently through Dynamic Programming.
We demonstrate superior segmentation accuracy compared to state-of-the-art superpixel algorithms regarding obstacles and freespace in the scene, evaluated on a large dataset captured in real-world traffic.

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Metadata
Title
Object-Level Priors for Stixel Generation
Authors
Marius Cordts
Lukas Schneider
Markus Enzweiler
Uwe Franke
Stefan Roth
Copyright Year
2014
Book
Pattern Recognition

Print ISBN: 978-3-319-11751-5

Electronic ISBN: 978-3-319-11752-2

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-11752-2_14

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