Authors:
Helmut Grabner
;
Christian Leistner
and
Horst Bischof
Affiliation:
Institute for Computer Graphics and Vision, Graz University of Technology, Austria
Keyword(s):
On-line learning, boosting, background modeling.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Software Engineering
;
Video Analysis
Abstract:
In modern video surveillance systems change and outlier detection is of highest interest. Most of these systems are based on standard pixel-by-pixel background modeling approaches. In this paper, we propose a novel robust block-based background model that is suitable for outlier detection using an extension to on-line boosting for feature selection. In order to be robust our system incorporates several novelties for previous proposed on-line boosting algorithms and classifier-based background modeling systems. We introduce time-dependency and control for
on-line boosting. Our system allows for automatically adjusting its temporal behavior to the underlying scene by using a control system which regulates the model parameters. The benefits of our approach are illustrated on several experiments on challenging standard datasets.