Social Force Model for Pedestrian Dynamics
Dirk Helbing,Péter Molnár +1 more
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TLDR
Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.Abstract:
It is suggested that the motion of pedestrians can be described as if they would be subject to ``social forces.'' These ``forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.read more
Citations
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Journal ArticleDOI
On the modeling of pedestrian motion
TL;DR: A model for the simulation of pedestrian flows and crowd dynamics has been developed that performs well for standard benchmarks, and allows for typical crowd dynamics, such as lane forming, overtaking, avoidance of obstacles and panic behaviour.
Journal ArticleDOI
Crowd behavior analysis
TL;DR: A review of the state-of-the-art methods in crowd behavior analysis from the physics and biologically inspired perspectives is provided, providing insights and comprehensive discussions for a broader understanding of the underlying prospect of blending physics and biology studies in computer vision.
Journal ArticleDOI
Pedestrian flows in bounded domains with obstacles
Benedetto Piccoli,Andrea Tosin +1 more
TL;DR: A discrete-time Eulerian model, in which the space occupancy by pedestrians is described via a sequence of Radon-positive measures generated by a push-forward recursive relation, which is suitable to address two-dimensional applications of practical interest, chiefly the motion of pedestrians in complex domains scattered with obstacles.
Proceedings ArticleDOI
Semi-supervised Learning for Anomalous Trajectory Detection
TL;DR: An incremental semi-supervised one-class learning procedure in which unlabelled trajectories are combined with occasional examples of normal behaviour labelled by a human operator is found to be effective on two different datasets, indicating that a human operators could potentially train the system to detect anomalous behaviour by providing only occasional interventions.
Book ChapterDOI
It Is Not the Journey But the Destination: Endpoint Conditioned Trajectory Prediction
Karttikeya Mangalam,Harshayu Girase,Shreyas Agarwal,Kuan-Hui Lee,Ehsan Adeli,Jitendra Malik,Adrien Gaidon +6 more
TL;DR: In this paper, a non-local social pooling layer enables PECNet to infer diverse yet socially compliant trajectories, which improves diversity and multi-modal trajectory prediction performance.
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