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Behavioral Digital Twins for Smart Cities
Digital twin is a virtual environment that allows us to simulate real world problems. For digital twin, one of the most important factors is human behavior modeling since it is required by many digital twin applications, such as crime and accident prevention, mitigation strategies in natural disasters, autonomous driving, health coaching and sports simulation. However, because of the complexity of human behavior, there are still many challenges unsolved. To address the challenges, multiple research fields need to be involved together, including computer vision, behavior science, human-computer interaction and AR/VR. This topic is germane to both computer vision and computational behavior communities. In this workshop, we aim to facilitate further discussion on this emerging research field from both technological and application perspectives. The outcomes of this workshop are relevant to building behavioral digital twins of pedestrians in smart cities equipped with sensor networks. During natural disasters, such as the COVID-19 pandemic, modeling behavioral patterns may lead to mitigation policies with greater efficiency and effectiveness, can identify cost-effective solutions to deliver public services, improve government accountability vis-`a-vis citizens and track progress and impact. This workshop will serve as a catalyst to bring diverse stakeholders together so that new scientific languages/thoughts can be established, in an effort to address the societal challenges of creating behavior sensing systems that account for the diversity of people and their environments.
Organizers: Koichiro Niinuma, Laszlo A. Jeni, Takahisa Yamamoto, Ryosuke Kawamura