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A Graphical Model for Rapid Obstacle Image-Map Estimation from Unmanned Surface Vehicles

  • Matej KristanEmail author
  • Janez Perš
  • Vildana Sulič
  • Stanislav Kovačič
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

Obstacle detection plays an important role in unmanned surface vehicles (USV). Continuous detection from images taken onboard the vessel poses a particular challenge due to the diversity of the environment and the obstacle appearance. An obstacle may be a floating piece of wood, a scuba diver, a pier, or some other part of a shoreline. In this paper we tackle this problem by proposing a new graphical model that affords a fast and continuous obstacle image-map estimation from a single video stream captured onboard a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and runs faster than real-time. We also present a new, challenging, dataset for segmentation and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model compares favorably in accuracy to the related approaches, requiring a fraction of computational effort.

Keywords

Markov Random Field Obstacle Detection Dynamic Obstacle Unmanned Ground Vehicle Water Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

This work was supported in part by the Slovenian research agency programs P2-0214, P2-0094, and projects J2-4284, J2-3607, J2-2221. We also thank HarphaSea d.o.o. for their hardware used to capture the dataset.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Matej Kristan
    • 1
    • 2
    Email author
  • Janez Perš
    • 1
  • Vildana Sulič
    • 1
  • Stanislav Kovačič
    • 1
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaSloveniaLjubljana
  2. 2.Faculty of Electrical EngineeringUniversity of LjubljanaSloveniaLjubljana

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