Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding

  • Timo Scharwächter
  • Markus Enzweiler
  • Uwe Franke
  • Stefan Roth
Conference paper

DOI: 10.1007/978-3-319-10602-1_35

Volume 8693 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Scharwächter T., Enzweiler M., Franke U., Roth S. (2014) Stixmantics: A Medium-Level Model for Real-Time Semantic Scene Understanding. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8693. Springer, Cham

Abstract

In this paper we present Stixmantics, a novel medium-level scene representation for real-time visual semantic scene understanding. Relevant scene structure, motion and object class information is encoded using so-called Stixels as primitive elements. Sparse feature-point trajectories are used to estimate the 3D motion field and to enforce temporal consistency of semantic labels. Spatial label coherency is obtained by using a CRF framework.

The proposed model abstracts and aggregates low-level pixel information to gain robustness and efficiency. Yet, enough flexibility is retained to adequately model complex scenes, such as urban traffic. Our experimental evaluation focuses on semantic scene segmentation using a recently introduced dataset for urban traffic scenes. In comparison to our best baseline approach, we demonstrate state-of-the-art performance but reduce inference time by a factor of more than 2,000, requiring only 50 ms per image.

Keywords

semantic scene understanding bag-of-features region classification real-time stereo vision stixels 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Timo Scharwächter
    • 1
    • 2
  • Markus Enzweiler
    • 1
  • Uwe Franke
    • 1
  • Stefan Roth
    • 2
  1. 1.Environment PerceptionDaimler R&DSindelfingenGermany
  2. 2.Department of Computer ScienceTU DarmstadtGermany