Towards Predicting Pedestrian Paths: identifying Surroundings from Monocular Video

• Cruz, José Aleixo ^{[1] [2]} ; Rúbio, Thiago R. P. M. ^{[1] [2]} ; João Jacob ^{[1] [2]} ; Daniel Garrido ^{[1] [2]} ; Cardoso, Henrique Lopes ^{[1] [2]} ; Daniel Silva ^{[1] [2]} ; Rui Rodrigues ^{[1] [2]}
  1. [1] Universidade Do Porto

     Universidade Do Porto

     Santo Ildefonso, Portugal

     
  2. [2] Artificial Intelligence and Computer Science Laboratory (LIACC; Porto, Portugal)
• Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.), David Camacho Fernández (ed. lit.), Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 594-601
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • Pedestrian behavior is an essential subject of study when developing or enhancing urban infrastructure. However, most behavior elicitation techniques are inherently bound to be biased by either the observer, the subject, or the environment. The SIMUSAFE project aims at collecting road users’ behavioral data in naturalistic and realistic scenarios to produce more accurate decision-making models. Using video captured from a monocular camera worn by a pedestrian, we employ machine learning and computer vision techniques to identify areas of interest surrounding a pedestrian. Namely, we use object detection and depth estimation to generate a map of obstacles that may influence the pedestrian’s actions. Our methods have shown to be successful in detecting free and occupied areas from monocular video.