An evaluation of attention models for use in SLAM

Samuel Dodge, Lina Karam

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we study the application of visual saliency models for the simultaneous localization and mapping (SLAM) problem. We consider visual SLAM, where the location of the camera and a map of the environment can be generated using images from a single moving camera. In visual SLAM, the interest point detector is of key importance. This detector must be invariant to certain image transformations so that features can be matched across di erent frames. Recent work has used a model of human visual attention to detect interest points, however it is unclear as to what is the best attention model for this purpose. To this aim, we compare the performance of interest points from four saliency models (Itti, GBVS, RARE, and AWS) with the performance of four traditional interest point detectors (Harris, Shi-Tomasi, SIFT, and FAST). We evaluate these detectors under several di erent types of image transformation and nd that the Itti saliency model, in general, achieves the best performance in terms of keypoint repeatability.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Intelligent Robots and Computer Vision XXXI
Subtitle of host publicationAlgorithms and Techniques
StatePublished - 2014
EventIntelligent Robots and Computer Vision XXXI: Algorithms and Techniques - San Francisco, CA, United States
Duration: Feb 4 2014Feb 6 2014

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherIntelligent Robots and Computer Vision XXXI: Algorithms and Techniques
Country/TerritoryUnited States
CitySan Francisco, CA


  • Interest Points
  • SLAM
  • Saliency
  • Visual Attention

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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