People tracking with human motion predictions from social forces

By Matthias Luber, Johannes Andreas Stork, Gian Diego Tipaldi, Kai Oliver Arras. IEEE International Conference on Robotics and Automation, 2010

355 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/3d48e6ed5af44bd6650601b8d9b91b4fa662b9a2

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Citations in the corpus

SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction (2021, background): “…the observed trajectories of pedestrians, pedestrian trajectory prediction aims to predict a sequence of future location coordinates of pedestrians, which plays a critical role in various applications like autonomous driving [3, 29], video surveillance [28, 45] and visual recognition [9, 27, 16].”
Peeking Into the Future: Predicting Future Person Activities and Locations in Videos (2019, background): “It is regarded as an essential building block in video understanding because looking at the visual information from the past to predict the future is useful in many applications like self-driving cars, socially-aware robots [19], etc.”
Core Challenges of Social Robot Navigation: A Survey (2021, background): “Over the more than 20 years following the RHINO and MINERVA deployments, many exciting research developments have emerged, for instance: Many lab studies and field deployments have contributed important empirical knowledge about robot navigation in crowds [18, 72, 124, 131, 165, 249]; the study and ”
Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation (2015, methodology): “In addition, this joint collision avoidance criteria has been used to improve the data association and target tracking of individuals in human crowds (Pellegrini et al., 2009, 2010; Luber et al., 2010).”
Who are you with and where are you going? (2011, background): “The pedestrian behavior model has been extensively studied in the ﬁelds where simulation plays an important role, such as graphics [5], and civil engineering [10] [11], or where accurate prediction is required, such as robotics [13, 6].”
Human motion trajectory prediction: a survey (2019, methodology): “Single-model methods deﬁne a single dynamical motion model, e.g. (Elnagar 2001; Zernetsch et al. 2016; Luber et al. 2010; Coscia et al. 2018; Pellegrini et al. 2009; Yamaguchi et al. 2011; Aoude et al. 2010; Petrich et al. 2013) 1.2.”
Social LSTM: Human Trajectory Prediction in Crowded Spaces (2016, methodology): “The ability to model these rules and use them to understand and predict human motion in complex real world environments is extremely valuable for a wide range of applications - from the deployment of socially-aware robots [41] to the design of intelligent tracking systems [43] in smart environments.”
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks (2018, methodology): “Over the past decades, this method has been often revisited [5, 6, 25, 26, 30, 31, 36, 46].”
Unfreezing the robot: Navigation in dense, interacting crowds (2010, background): “This joint collision avoidance criteria has been exploited to improve the data association and target tracking of individuals in human crowds ([18], [19], [16]).”
Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction (2020, background): “We predict the future trajectories in a single pass. pedestrian trajectories is critical in helping identifying suspicious activities [15, 28, 20].”
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes (2016, methodology): “used in robotics [28], and in the context of target tracking [1,2,29,30,31,32,33].”