Classification of a Pedestrian’s Behaviour Using Dual Deep Neural Networks

James Spooner, Madeline Cheah, Vasile Palade, Stratis Kanarachos, Alireza Daneshkhah

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review


Vulnerable road user safety is of paramount importance as transport moves towards fully autonomous driving. The research question posed by this research is of how can we train a computer to be able to see and perceive a pedestrian’s movement. This work presents a dual network architecture, trained in tandem, which is capable of classifying the behaviour of a pedestrian from a single image with no prior context. The results show that the most successful network was able to achieve a correct classification accuracy of 94.3% when classifying images based on their behaviour. This shows the use of a novel data fusion method for pedestrian images and human poses. Having a network with these capabilities is important for the future of transport, as it will allow vehicles to correctly perceive the intention of pedestrians crossing the street, and will ultimately lead to fewer pedestrian casualties on our roads.

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2020 Computing Conference
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
Number of pages17
ISBN (Electronic)978-3-030-52243-8
ISBN (Print)978-3-030-52242-1
Publication statusE-pub ahead of print - 4 Jul 2020
Externally publishedYes
EventScience and Information Conference, SAI 2020 - London, United Kingdom
Duration: 16 Jul 202017 Jul 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1230 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceScience and Information Conference, SAI 2020
Country/TerritoryUnited Kingdom


  • Classification
  • Deep learning
  • Neural networks
  • Pedestrian prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)


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