Authors:
Florian Particke
1
;
Lucila Patiño-Studencki
1
;
Jörn Thielecke
1
and
Christian Feist
2
Affiliations:
1
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
;
2
Audi Electronics Venture GmbH, Germany
Keyword(s):
Object Tracking, Pedestrians, Surveillance, Pedestrian Trajectory Pattern, Parametric Model.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
Mobile robots and autonomous driving cars operate in a shared environment with pedestrians. In order to
avoid accidents, it is important to track and predict human trajectories in an optimal way. In this paper, a
generalized potential field approach for characterizing pedestrian movements is proposed which goes beyond
the well-known social force model. Its goal is to give a generalized architecture for improving the tracking
accuracy of pedestrians in surveillance situations. In comparison to other fusion approaches, the number of
proposed parameters is reduced and the parameters can be intuitively understood. For a simple scenario, in
a forum the trajectories of pedestrians are predicted for a configured parameter set. For this purpose, the
proposed model is used. The predicted trajectories are compared to the real trajectories of the pedestrians.
First results regarding the accuracy of the approach are presented.