Journal Article10.1111/J.1467-8659.2007.01089.X
Crowds by Example
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TL;DR: By learning from real‐world examples, autonomous agents display complex natural behaviors that are often missing in crowd simulations.
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Abstract: We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.
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Citations
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alexandre Alahi,Kratarth Goel,Vignesh Ramanathan,Alexandre Robicquet,Li Fei-Fei,Silvio Savarese +5 more
- 27 Jun 2016
TL;DR: This work proposes an LSTM model which can learn general human movement and predict their future trajectories and outperforms state-of-the-art methods on some of these datasets.
Abnormal crowd behavior detection using social force model
Ramin Mehran,Alexis Oyama,Mubarak Shah +2 more
- 20 Jun 2009
TL;DR: A novel method to detect and localize abnormal behaviors in crowd videos using Social Force model and it is shown that the social force approach outperforms similar approaches based on pure optical flow.
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TL;DR: A model of dynamic social behavior, inspired by models developed for crowd simulation, is introduced, trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera.
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints
Amir Sadeghian,Vineet Kosaraju,Ali Sadeghian,Noriaki Hirose,Hamid Rezatofighi,Silvio Savarese +5 more
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TL;DR: In this paper, an interpretable framework based on Generative Adversarial Network (GAN) is proposed for path prediction for multiple interacting agents in a scene, which leverages two sources of information, the path history of all the agents in the scene, and the scene context information, using images of the scene.
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes
Alexandre Robicquet,Amir Sadeghian,Alexandre Alahi,Silvio Savarese +3 more
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