Continuum crowds

By Adrien Treuille, Seth Cooper, Zoran Popovic. ACM Transactions on Graphics, 2006

1,008 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/f6c6ae791bf0696a4053e171fd27918e8fc17ab3

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Citations in the corpus

You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions (2019, methodology): “s [24, 10]. Beyond body pose, there is a growing interest in modeling human-human interactions [49, 22, 34] to predict pedestrian trajectories [1, 37, 2], analyze social behavior and group activities [37, 51, 6, 14, 22], and understand human-object interactions [53, 18, 12]. Our method also capitali”
Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models (2016, background): “[7] utilize continuum dynamics and potential fields to model naturally moving crowds.”
On the Modeling of Traffic and Crowds: A Survey of Models, Speculations, and Perspectives (2011, neutral): “Further analysis is given in [48], [206].”
STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction (2019, background): “proposed continuum dynamics [31], and further, there are a series of topics models [35, 15, 9].”
Abnormal crowd behavior detection using social force model (2009, background): “eling of the crowd movements is used to create realistic special effects of crowds without the need for human actors[25][9][20][27].”
Core Challenges of Social Robot Navigation: A Survey (2021, methodology): “PedSim [74], OpenSteer [184], Menge [43], Continuum [228], both written in C++, are the most common tools used to simulate crowd scenarios.”
Reciprocal Velocity Obstacles for real-time multi-agent navigation (2008, background): “There is also an extensive amount of literature on multiagent navigation, in which each agent navigates individually among the other agents, which are considered as obstacles, e.g. [13], [19], [17], [6], [12].”
Social LSTM: Human Trajectory Prediction in Crowded Spaces (2016, neutral): “[62] use continuum dynamics, Antonini et.”
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents (2018, methodology): “Some similar methods have also been proposed that use continuum dynamics (Treuille et al. 2006), Gaussian processes (Wang et al. 2008), etc. Bera et al. (2016; 2017) combine an Ensemble Kalman Filter and human motion model to predict the trajectories for crowds.”
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents (2012, background): “It has important applications, such as event recognition [12], traffic flow estimation [23], behavior prediction [2], and crowd simulation [20].”
A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft (2013, neutral): “In recent commercial RTS games like StarCraft 2 or Supreme Commander 2, flocking-like behaviors have been inspired by continuum crowds (flow field) [62].”
A synthetic-vision based steering approach for crowd simulation (2010, background): “Crowd simulation was also studied as a flowing continuum [Hughes 2003; Treuille et al. 2006] that allows simulating numerous walkers in real-time.”
Crowded Scene Analysis: A Survey (2015, methodology): “For the continuum-based approach, the crowd is treated as a physical ﬂuid with particles, thus a lot of analytical methods from statistical mechanics and thermodynamics are introduced [43], [44].”
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks (2018, neutral): “[42] use continuum dynamics, and Wang et.”