To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Context-free Self-Conditioned GAN for Trajectory Forecasting
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0001-9059-6175
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-6566-3097
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-3908-4921
2022 (English)In: 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022: Proceedings / [ed] Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY, IEEE, 2022, p. 1218-1223Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a context-free unsupervised approach based on a self-conditioned GAN to learn different modes from 2D trajectories. Our intuition is that each mode indicates a different behavioral moving pattern in the discriminator's feature space. We apply this approach to the problem of trajectory forecasting. We present three different training settings based on self-conditioned GAN, which produce better forecasters. We test our method in two data sets: human motion and road agents. Experimental results show that our approach outperforms previous context-free methods in the least representative supervised labels while performing well in the remaining labels. In addition, our approach outperforms globally in human motion, while performing well in road agents.

Place, publisher, year, edition, pages
IEEE, 2022. p. 1218-1223
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-106182DOI: 10.1109/ICMLA55696.2022.00196ISI: 000980994900186Scopus ID: 2-s2.0-85152213391ISBN: 9781665462839 (electronic)OAI: oai:DiVA.org:oru-106182DiVA, id: diva2:1763381
Conference
21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), Nassau, Bahamas, December 12-14, 2022
Funder
Knut and Alice Wallenberg FoundationAvailable from: 2023-06-07 Created: 2023-06-07 Last updated: 2023-06-07Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Almeida, Tiago Rodrigues deGutiérrez Maestro, EduardoMartinez Mozos, Oscar

Search in DiVA

By author/editor
Almeida, Tiago Rodrigues deGutiérrez Maestro, EduardoMartinez Mozos, Oscar
By organisation
School of Science and Technology
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 64 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf