Automatic estimation of parametric saliency maps (PSMs) for autonomous pedestrians

Melissa Kremer, Peter Caruana, Brandon Haworth, Mubbasir Kapadia, Petros Faloutsos

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Modelling visual attention is an important aspect of simulating realistic virtual humans. This work proposes a parametric model and method for generating real-time saliency maps from the perspective of virtual agents which approximate those of vision-based saliency approaches. The model aggregates a saliency score from user-defined parameters for objects and characters in an agent's view and uses that to output a 2D saliency map which can be modulated by an attention field to incorporate 3D information as well as a character's state of attentiveness. The aggregate and parameterized structure of the method allows the user to model a range of diverse agents. The user may also expand the model with additional layers and parameters. The proposed method can be combined with normative and pathological models of the human visual field and gaze controllers, such as the recently proposed model of egocentric distractions for casual pedestrians that we use in our results. This is an extended version of a short paper published in MIG 2021. The extension includes an optimization approach that fits the parameters of the proposed model to established saliency models such as SALICON using a much larger and more realistic urban test set.

Original languageEnglish (US)
Pages (from-to)86-94
Number of pages9
JournalComputers and Graphics (Pergamon)
StatePublished - May 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Engineering(all)
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


  • Crowd simulation
  • Saliency
  • Visual attention


Dive into the research topics of 'Automatic estimation of parametric saliency maps (PSMs) for autonomous pedestrians'. Together they form a unique fingerprint.

Cite this