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THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction
Örebro University, School of Science and Technology.ORCID iD: 0000-0001-9059-6175
Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-9387-2312
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-1298-5607
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2023 (English)In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), IEEE, 2023, p. 2192-2201Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.

Place, publisher, year, edition, pages
IEEE, 2023. p. 2192-2201
Series
IEEE International Conference on Computer Vision Workshop (ICCVW), ISSN 2473-9936, E-ISSN 2473-9944
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-109508DOI: 10.1109/ICCVW60793.2023.00234ISI: 001156680302028Scopus ID: 2-s2.0-85182932549ISBN: 9798350307450 (print)ISBN: 9798350307443 (electronic)OAI: oai:DiVA.org:oru-109508DiVA, id: diva2:1808690
Conference
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Paris, France, October 2-6, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP), NT4220EU, Horizon 2020, 101017274 (DARKO)Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2024-03-25Bibliographically approved

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Almeida, TiagoSchreiter, TimZhu, YufeiGutiérrez Maestro, EduardoMorillo-Mendez, LucasMartinez Mozos, OscarMagnusson, MartinLilienthal, Achim

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Almeida, TiagoSchreiter, TimZhu, YufeiGutiérrez Maestro, EduardoMorillo-Mendez, LucasMartinez Mozos, OscarMagnusson, MartinLilienthal, Achim
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School of Science and Technology
Computer Vision and Robotics (Autonomous Systems)

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