Abstract
In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained from sensors, like cameras, destinations cannot be observed directly. Our research question is: Can we obtain this information from video data using machine learning methods? We use density heatmaps, which indicate the pedestrian density within a given camera cutout, as input to predict the destination distributions. For our proof of concept, we train a Random Forest predictor on an exemplary data set generated with the Vadere microscopic simulator. The scenario is a crossroad where pedestrians can head left, straight or right. In addition, we gain first insights on suitable placement of the camera. The results motivate an in-depth analysis of the methodology.
Highlights
It is a shared goal of crowd simulation experts to look into the future for at least a few minutes to predict dangers like extremely high densities that might evolve
Like positions, speeds and densities can be obtained from cameras, even if the speed and accuracy with which the data is acquired may be insufficient for prediction at the moment
In this publication, we deliver a proof of concept for configuring a machine learning method to obtain the destinations of pedestrians based on density heatmaps
Summary
It is a shared goal of crowd simulation experts to look into the future for at least a few minutes to predict dangers like extremely high densities that might evolve. Like positions, speeds and densities can be obtained from cameras, even if the speed and accuracy with which the data is acquired may be insufficient for prediction at the moment. Agent-based microscopic crowd simulations use destinations to navigate pedestrians. This holds especially for all simulations based on a floor field. Destinations are a crucial input parameter for the simulation. A previous study has shown that the destinations of pedestrians have a high impact on the simulation output [1]
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