Abstract
During the COVID-19 pandemic, signs and barriers were used to guide people through spaces in accordance with social distancing mandates. Such measures were unable to accommodate for fluctuating traffic and complexities of human behavior – leading to inconsistent effectiveness and a diminishing of social life in once-active public spaces. Toward a more flexible and engaging means of guidance, this research brings together computer vision, human behavioral modeling, and light projection to create an installation that suggests paths to pedestrians in real-time. Through the processing of positional data, it predicts optimal trajectories for each pedestrian that accounts for the movement of others and the environment around them. The projected visuals then present these animated paths as part of a fluid and open-ended spatial interface that aims to improve pedestrian efficiency while sparking social interactions. In a month-long installation of the system in a frequently trafficked space, we observed individuals serendipitously connect through the projections. Though limitations in the scale of the installation constrained its depth, Social Signals has the potential to improve the built environment not through material interventions but rather through highlighting human interactions.
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Duong, E., Parascho, S. (2023). Social Signals: An Adaptive Installation for Mediating Space During COVID-19 and Beyond. In: Turrin, M., Andriotis, C., Rafiee, A. (eds) Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries. CAAD Futures 2023. Communications in Computer and Information Science, vol 1819. Springer, Cham. https://doi.org/10.1007/978-3-031-37189-9_36
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DOI: https://doi.org/10.1007/978-3-031-37189-9_36
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