Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Long-term path prediction in urban scenarios using circular distributions
 
research article

Long-term path prediction in urban scenarios using circular distributions

Coscia, Pasquale
•
Castaldo, Francesco
•
Palmieri, Francesco
Show more
2018
Image and Vision Computing

Human ability to foresee the near future plays a key role in everyone's life to prevent potentially dangerous situations. To be able to make predictions is crucial when people have to interact with the surrounding environment. Modeling such capability can lead to the design of automated warning systems and provide moving robots with an intelligent way of interaction with changing situation. In this work we focus on a typical urban human-scene where we aim at predicting an agent's behavior using a stochastic model. In this approach, we fuse the various factors that would contribute to a human motion in different contexts. Our method uses previously observed trajectories to build point-wise circular distributions that after combination, provide a statistical smooth prediction towards the most likely areas. More specifically, a ray-launching procedure, based on a semantic segmentation, gives a coarse scene representation for collision avoidance; a nearly-constant velocity dynamic model smooths the acceleration progression and knowledge of the agent's destination may further steer the path prediction.

  • Details
  • Metrics
Type
research article
DOI
10.1016/j.imavis.2017.11.006
Author(s)
Coscia, Pasquale
Castaldo, Francesco
Palmieri, Francesco
Alahi, Alexandre  
Savarese, Silvio
Ballan, Lamberto
Date Issued

2018

Published in
Image and Vision Computing
Volume

69

Start page

81

End page

91

Subjects

Long-term path prediction

•

Circular distribution

•

Human-scene interaction

•

Stochastic model

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
VITA  
Available on Infoscience
May 11, 2018
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/146394
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés