Generation of pedestrian pose structures using generative adversarial networks

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

The safety of vulnerable road users is of paramount importance as transport moves towards fully automated driving. The richness of real-world data required for testing autonomous vehicles is limited, and furthermore, the available data does not have a fair representation of different scenarios and rare events. This work presents a novel approach for the generation of human pose structures, specifically the type of pose structures that would appear to be in pedestrian scenarios. The results show that the generated pedestrian structures are indistinguishable from the ground truth pose structures when classified using a suitably trained classifier. The paper demonstrates that the Generative Adversarial Network architecture can be used to create realistic new training samples, and, in future, new pedestrian events.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
EditorsM. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1644-1650
Number of pages7
ISBN (Electronic)9781728145495
DOIs
Publication statusPublished - 17 Feb 2020
Event18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 - Boca Raton, United States
Duration: 16 Dec 201919 Dec 2019

Publication series

NameProceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

Conference

Conference18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019
CountryUnited States
CityBoca Raton
Period16/12/1919/12/19

Keywords

  • Autonomous Driving
  • GANs
  • Neural Networks
  • Pedestrians
  • Pose estimation

ASJC Scopus subject areas

  • Strategy and Management
  • Artificial Intelligence
  • Computer Science Applications
  • Decision Sciences (miscellaneous)
  • Signal Processing
  • Media Technology

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  • Cite this

    Spooner, J., Cheah, M., Palade, V., Kanarachos, S., & Daneshkhah, A. (2020). Generation of pedestrian pose structures using generative adversarial networks. In M. A. Wani, T. M. Khoshgoftaar, D. Wang, H. Wang, & N. Seliya (Eds.), Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019 (pp. 1644-1650). [8999199] (Proceedings - 18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2019.00269