Item

Abstract

Current driver assistance systems support the visual recognition of pedestrians in dynamic visual scenes, typically based on the detection of static and dynamic cues. The integration of knowledge about more complex interactions between groups of humans into such systems is still a challenge. This workshop aims at presenting interdisciplinary research and developments on human collective behavior analysis and synthesis, ranging from motion-capturing, graphical simulation for prototyping and experiments, machine-vision for detection and prediction, to robotics for safe navigation in crowded urban areas. Novel approaches at the interface of different disciplines with similar aims are contributing to a better understanding of dynamic human crowd behavior with the hope to ultimately lead to even more purposeful human-centered
assistance systems. At the core of these approaches are generative models of social interactions learned from real-world data, applied to the simulation of populated scenes and machine vision. The overall goal of this workshop is to give an overview and stimulate research in the development of new models and simulation tools for system prototyping, ultimately leading to assistance systems that can take complex behavior of pedestrians in street scenes into account.