Fixation Prediction and Visual Priority Maps for Biped Locomotion

Pui Anantrasirichai*, Katherine Daniels, J Burn, Iain Gilchrist, David Bull

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)
319 Downloads (Pure)


This paper presents an analysis of the low-level features and key spatial points used by humans during locomotion over diverse types of terrain. Although, a number of methods for creating saliency maps and task-dependent approaches have been proposed to estimate the areas of an image that attract human attention, none of these can straightforwardly be applied to sequences captured during locomotion, which contain dynamic content derived from a moving viewpoint. We used a novel learning-based method for creating a visual priority map informed by human eye tracking data. Our proposed priority map is created based on two fixation types: first exploiting the observation that humans search for safe foot placement and second that they observe the edges of a path as a guide to safe traversal of the terrain. Texture features and the difference between them, observed at the region around an eye position, are employed within a support vector machine to create a visual priority map for biped locomotion. The results show that our proposed method outperforms the state-of-the-art, particularly for more complex terrains, where achieving smooth locomotion needs more attention on the traversing path.

Original languageEnglish
Pages (from-to)2294-2306
Number of pages13
JournalIEEE Transactions on Cybernetics
Issue number8
Early online date26 Sept 2017
Publication statusPublished - Aug 2018

Structured keywords

  • Cognitive Science
  • Visual Perception


  • Bioinspired
  • eye tracking
  • locomotion
  • priority map
  • salience


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