Authors:
Antoine Fagette
1
;
Patrick Jamet
1
;
Daniel Racoceanu
2
and
Jean-Yves Dufour
3
Affiliations:
1
Thales Solutions Asia Pte Ltd and CNRS, Singapore
;
2
University Pierre and Marie Curie Sorbonne Universities and CNRS, France
;
3
Thales Services, France
Keyword(s):
Particle Video, Crowd, Flow Tracking, Entry-Exit Areas Detection, Occlusions, Entry-Exit Areas Linkage.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Understanding
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Video Analysis
Abstract:
In this paper we interest ourselves to the problem of flow tracking for dense crowds. For this purpose, we use a
cloud of particles spread on the image according to the estimated crowd density and driven by the optical flow.
This cloud of particles is considered as statistically representative of the crowd. Therefore, each particle has
physical properties that enable us to assess the validity of its behavior according to the one expected from a
pedestrian and to optimize its motion dictated by the optical flow. This leads us to three applications described
in this paper: the detection of the entry and exit areas of the crowd in the image, the detection of dynamic
occlusions and the possibility to link entry areas with exit ones according to the flow of the pedestrians. We
provide the results of our experimentation on synthetic data and show promising results.