Interactivity, Game Creation, Design, Learning, and Innovation. 7th EAI International Conference, ArtsIT 2018, and 3rd EAI International Conference, DLI 2018, ICTCC 2018, Braga, Portugal, October 24–26, 2018, Proceedings

Research Article

Using Motion Expressiveness and Human Pose Estimation for Collaborative Surveillance Art

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  • @INPROCEEDINGS{10.1007/978-3-030-06134-0_12,
        author={Jonas Billeskov and Tobias M\`{u}ller and Georgios Triantafyllidis and George Palamas},
        title={Using Motion Expressiveness and Human Pose Estimation for Collaborative Surveillance Art},
        proceedings={Interactivity, Game Creation, Design, Learning, and Innovation. 7th EAI International Conference, ArtsIT 2018, and 3rd EAI International Conference, DLI 2018, ICTCC 2018, Braga, Portugal, October 24--26, 2018, Proceedings},
        proceedings_a={ARTSIT \& DLI},
        year={2019},
        month={1},
        keywords={Generative art Surveillance data Motion expressiveness Data transformation Human pose estimation 3D visualization},
        doi={10.1007/978-3-030-06134-0_12}
    }
    
  • Jonas Billeskov
    Tobias Møller
    Georgios Triantafyllidis
    George Palamas
    Year: 2019
    Using Motion Expressiveness and Human Pose Estimation for Collaborative Surveillance Art
    ARTSIT & DLI
    Springer
    DOI: 10.1007/978-3-030-06134-0_12
Jonas Billeskov1,*, Tobias Møller1,*, Georgios Triantafyllidis1,*, George Palamas1,*
  • 1: Aalborg University Copenhagen
*Contact email: tnma14@student.aau.dk, jbille14@student.aau.dk, gt@create.aau.dk, gpa@create.aau.dk

Abstract

Surveillance art is a contemporary art practice that deals with the notion of human expressiveness in public spaces and how monitoring data can be transformed into more poetic forms, unleashing their creative potential. Surveillance, in a sociopolitical context, is a participatory activity that has changed radically in recent years and could be argued to produce, not only social control but also to contribute to the formation of a collective image of feelings and affects expressed in modern societies. The paper explores a multidisciplinary approach based on tracking human motion from surveillance cameras on New York Time Square. The performed human trajectories were tracked with a real-time machine vision framework and the outcomes were used to feed a generative design algorithm in order to transform the data into emotionally expressive 3D visualizations. Finally, a study was conducted to assess the expressive power of this approach so as to better understand the relationships among perceived affective qualities and human behaviors.