Authors:
Olivier Chabiron
1
;
Jérôme Fehrenbach
1
;
Pierre Degond
1
;
Mehdi Moussaïd
2
;
Julien Pettré
3
and
Samuel Lemercier
3
Affiliations:
1
Paul Sabatier University, France
;
2
Université Paul Sabatier and CNRS, France
;
3
INRIA Rennes - Bretagne Atlantique, France
Keyword(s):
Clustering, Classification, Motion and tracking, Detection, Graphs, Entropy, Complex systems.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Bioinformatics and Systems Biology
;
Classification
;
Clustering
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Theory and Methods
Abstract:
This paper proposes a distance measurement between pedestrian trajectories. This distance is used in a clustering method aiming to detect lanes of pedestrians in experimental data. The main ingredient is to take full advantage of the time sequence available. A study of the sensitivity of the clustering to the parameters shows it is possible to choose a stable set of parameters. We also define an order index based on the concept of entropy. The potential of this index is illustrated in the case of pedestrian lane detection.