Authors:
M. Nieto
1
;
L. Unzueta
1
;
A. Cortés
1
;
J. Barandiaran
1
;
O. Otaegui
1
and
P. Sánchez
2
Affiliations:
1
Vicomtech-IK4 Research Alliance, Spain
;
2
IKUSI, Spain
Keyword(s):
Computer vision, Monocamera, Traffic flow surveillance, 3D modeling.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Detecting 3D Objects Using Patterns of Motion and Appearance
;
Human-Computer Interaction
;
Methodologies and Methods
;
Model-Based Object Tracking in Image Sequences
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Vision
;
Retrieval of 3D Objects from Video Sequences
Abstract:
A new method for 3D vehicle modeling in low-cost monocamera surveillance systems is introduced in this paper. The proposed algorithm aims to resolve the projective ambiguity of 2D image observations by means of the integration of temporal information and model priors within a Markov Chain Monte Carlo (MCMC) method. The method is specially designed to work in challenging scenarios, with noisy and blurred 2D observations, where traditional edge-fitting or feature-based methods fail. Tests have shown excellent estimation results for traffic-flow video surveillance applications, that can be used to classify vehicles according to their length, width and height.