Authors:
R. Reulke
1
;
S. Bauer
2
;
T. Döring
2
and
R. Spangenberg
2
Affiliations:
1
Humboldt-Universität zu Berlin, Institut für Informatik, Computer Vision, Germany
;
2
German Aerospace Center, Institute of Transportation Systems, Germany
Keyword(s):
Multi-camera sensing, fixed-viewpoint camera, cooperative distributed vision, multi-camera orientation, multi-target tracking.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Detecting 3D Objects Using Patterns of Motion and Appearance
;
Feature Extraction
;
Features Extraction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Image Registration
;
Implementation of Image and Video Processing Systems
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Model-Based Object Tracking in Image Sequences
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Multi-View Geometry
;
Pattern Recognition
;
Physiological Computing Systems
;
Real-Time Vision
;
Segmentation and Grouping
;
Signal Processing, Sensors, Systems Modeling and Control
;
Software Engineering
;
Tracking of People and Surveillance
;
Video Analysis
Abstract:
Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements).Different views of the same area by more than one camera sensor are necessary, because of the typical limitations of single camera systems, resulting from occlusions by other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras will be done by a tracking approach. This approach opens up opportunities to identify and specify traf
fic objects, their location, speed and other characteristic object information. The system creates new derived and consolidated information of traffic participants. Thus, also descriptions of individual traffic participants are possible.
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