Authors:
Marie-Neige Chapel
;
Erwan Guillou
and
Saida Bouakaz
Affiliation:
Université de Lyon, CNRS, Université Lyon 1 and LIRIS, France
Keyword(s):
Object Detection, Moving Camera, 3D Geometric Constraint, Statistical Analysis.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Pattern Recognition
;
Robotics
;
Segmentation and Grouping
;
Software Engineering
Abstract:
The detection of moving objects in the video stream of a moving camera is a complex task. Static objects
appear moving in the video stream as moving objects. Thus, it is difficult to identify motions that belong to
moving objects because they are hidden by those of static objects. To detect moving objects we propose a
novel geometric constraint based on 2D and 3D information. A sparse reconstruction of the visible part of the
scene is performed in order to detect motions in the 3D space where the scene perception is not deformed by
the camera motion. A first labeling estimation is performed in the 3D space and then apparent motions in the
video stream of the moving camera are used to validate the estimation. Labels are computed from confidence
values which are updated at each frame according to the geometric constraint. Our method can detect several
moving objects in complex scenes with high parallax.