Fetching the paper…
Reading the bibliography…
When humans navigate a crowed space such as a university campus or the sidewalks of a busy street, they follow common sense rules based on social etiquette.
Social force model for pedestrian dynamics
D. Helbing and P. Molnar · 1995
Earlier work this paper cites.
The flow of human crowds
R. L. Hughes · 2003
Earlier work this paper cites.
A discrete choice pedestrian behavior model for pedestrian detection in visual tracking systems
G. Antonini, S. Venegas, J.-P. Thiran, and M. Bierlaire · 2004
Earlier work this paper cites.
Probabilistic people tracking for occlusion handling
R. Cucchiara, C. Grana, G. Tardini, and R. Vezzani · 2004
Earlier work this paper cites.
Dependent gaussian processes
P. Boyle and M. Frean · 2005
Earlier work this paper cites.
Discrete choice models of pedestrian walking behavior
G. Antonini, M. Bierlaire, and M. Weber · 2006
Earlier work this paper cites.
Continuum crowds
A. Treuille, S. Cooper, and Z. Popović · 2006
Earlier work this paper cites.
Semantic-based surveillance video retrieval
W. Hu, D. Xie, Z. Fu, W. Zeng, and S. Maybank · 2007
Earlier work this paper cites.
Crowds by example
A. Lerner, Y. Chrysanthou, and D. Lischinski · 2007
Earlier work this paper cites.
Modelling smooth paths using gaussian processes
M. K. C. Tay and C. Laugier · 2008
Earlier work this paper cites.
Gaussian process dynamical models for human motion
J. M. Wang, D. J. Fleet, and A. Hertzmann · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey · 2008
Earlier work this paper cites.
What are they doing?: Collective activity classification using spatio-temporal relationship among people
W. Choi, K. Shahid, and S. Savarese · 2009
Earlier work this paper cites.
Observing human-object interactions: Using spatial and functional compatibility for recognition
A. Gupta, A. Kembhavi, and L. Davis · 2009
Earlier work this paper cites.
Abnormal crowd behavior detection using social force model
R. Mehran, A. Oyama, and M. Shah · 2009
Cited alongside, same era.
Automatic cluster number selection using a split and merge k-means approach
M. Muhr and M. Granitzer · 2009
Cited alongside, same era.
You’ll never walk alone: Modeling social behavior for multi-target tracking
S. Pellegrini, A. Ess, K. Schindler, and L. Van Gool · 2009
Cited alongside, same era.
A probabilistic model of human motion and navigation intent for mobile robot path planning
S. Thompson, T. Horiuchi, and S. Kagami · 2009
Cited alongside, same era.
Planning-based prediction for pedestrians
B. D. Ziebart, N. Ratliff, G. Gallagher, C. Mertz, K. Peterson, J. A. Bagnell, M. Hebert, A. K. Dey, and S. Srinivasa · 2009
Cited alongside, same era.
Learning to navigate through crowded environments
P. Henry, C. Vollmer, B. Ferris, and D. Fox · 2010
Who are you with and where are you going?
K. Yamaguchi, A. C. Berg, L. E. Ortiz, and T. L. Berg · 2011
Later among the works it cites.
A unified framework for multi-target tracking and collective activity recognition
W. Choi and S. Savarese · 2012
Later among the works it cites.
Activity forecasting
K. M. Kitani, B. D. Ziebart, J. A. Bagnell, and M. Hebert · 2012
Later among the works it cites.
Robot navigation in dense human crowds: the case for cooperation
P. Trautman, J. Ma, R. M. Murray, and A. Krause · 2013
Later among the works it cites.
Inferring” dark matter” and” dark energy” from videos
D. Xie, S. Todorovic, and S.-C. Zhu · 2013
Later among the works it cites.
Inferring “dark matter” and “dark energy” from videos
D. Xie, S. Todorovic, and S.-C. Zhu · 2013
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Beyond actions: Discriminative models for contextual group activities
T. Lan, Y. Wang, W. Yang, and G. Mori · 2010
Cited alongside, same era.
People tracking with human motion predictions from social forces
M. Luber, J. A. Stork, G. D. Tipaldi, and K. O. Arras · 2010
Cited alongside, same era.
Improving data association by joint modeling of pedestrian trajectories and groupings
S. Pellegrini, A. Ess, and L. Van Gool · 2010
Cited alongside, same era.
Unfreezing the robot: Navigation in dense, interacting crowds
P. Trautman and A. Krause · 2010
Cited alongside, same era.
Gaussian process regression flow for analysis of motion trajectories
K. Kim, D. Lee, and I. Essa · 2011
Cited alongside, same era.
Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker
L. Leal-Taixé, G. Pons-Moll, and B. Rosenhahn · 2011
Cited alongside, same era.
Socially-aware large-scale crowd forecasting
A. Alahi, V. Ramanathan, and L. Fei-Fei · 2014
Later among the works it cites.
Understanding collective activitiesof people from videos
W. Choi and S. Savarese · 2014
Later among the works it cites.
Learning to predict trajectories of cooperatively navigating agents
H. Kretzschmar, M. Kuderer, and W. Burgard · 2014
Later among the works it cites.
Learning an image-based motion context for multiple people tracking
L. Leal-Taixé, M. Fenzi, A. Kuznetsova, B. Rosenhahn, and S. Savarese · 2014
Later among the works it cites.
Visual tracking: an experimental survey
A. W. Smeulders, D. M. Chu, R. Cucchiara, S. Calderara, A. Dehghan, and M. Shah · 2014
Later among the works it cites.
Reasoning about object affordances in a knowledge base representation
Y. Zhu, A. Fathi, and L. Fei-Fei · 2014
Later among the works it cites.
Anticipating human activities using object affordances for reactive robotic response
H. S. Koppula and A. Saxena · 2015
Later among the works it cites.
Understanding pedestrian behaviors from stationary crowd groups
S. Yi, H. Li, and X. Wang · 2015
Later among the works it cites.