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Event Details

  • Monday, November 6, 2017
  • 09:15 - 10:15

KEYNOTE: Simulating and Sensing Pedestrian Movements and Behaviors

From record-setting crowds at rallies, robot swarms in the field, to avatars in social virtual reality (VR), our world is experiencing a continuing rise of complex, distributed systems of independently moving humans in physical and virtual worlds. With potential applications such as predicting crowd panic, improving human-robot cooperation, enhancing social interactions, conceptualizing urban layout, computational models to analyze, understand, predict, reproduce, and control collective behaviors of complex human dynamical systems are becoming critically important. In this talk, we will present an overview of work on simulating as well as sensing movements of pedestrians as part of a crowd. These include new velocity-space models to compute cooperative motion paths and behaviors for a group of independent agents, sharing the same physical world or virtual space. These techniques include optimization-based strategies for distributed collision avoidance, the principle of least effort for simulating crowds, and data-driven models for capturing differences in personalities. Furthermore, we use these navigation along with Personality Trait Theory to characterize the personality of pedestrians from crowd videos and identify anomalies in their behaviors. We highlight their applications to modeling the behavior and movements of pilgrims during Hajj, Presidential Inauguration crowds, Social VR, human-robot interaction, visual surveillance, and architectural analysis.