Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study

@inproceedings{Schneider2013PedestrianPP,
  title={Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study},
  author={Nicolas Schneider and Dariu Gavrila},
  booktitle={GCPR},
  year={2013}
}
In the context of intelligent vehicles, we perform a comparative study on recursive Bayesian filters for pedestrian path prediction at short time horizons (< 2s). We consider Extended Kalman Filters (EKF) based on single dynamical models and Interacting Multiple Models (IMM) combining several such basic models (constant velocity/acceleration/turn). These are applied to four typical pedestrian motion types (crossing, stopping, bending in, starting). Position measurements are provided by an… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 22 references

Design and Analysis of Modern Tracking Systems

S. Blackman, R. Popoli
Artech House Norwood, MA • 1999
View 6 Excerpts
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Early detection of the pedestrians intention to cross the street

S Köhler
Proc. of the IEEE ITSC. pp. 1759–1764 • 2012
View 1 Excerpt

Pedestrian Detection: An Evaluation of the State of the Art

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2012
View 1 Excerpt

Tracking of 2D or 3D Irregular Movement by a Family of Unscented Kalman Filters

J. Inform. and Commun. Convergence Engineering • 2012
View 2 Excerpts

EKF/UKF toolbox for Matlab

S. Särkkä, J. Hartikainen, A. Solin
http://becs.aalto.fi/en/research/bayes/ekfukf/ • 2011
View 1 Excerpt

Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision

IEEE Transactions on Intelligent Transportation Systems • 2009
View 2 Excerpts

Monocular Pedestrian Detection: Survey and Experiments

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2009
View 1 Excerpt

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