A multi-criteria model for robust foreground extraction

Authors:
A. Homayoun Kamkar-Parsi;Robert Laganière;Martin Bouchard
Affiliations:
University of Ottawa, Ottawa, Ontario, Canada;University of Ottawa, Ottawa, Ontario, Canada;University of Ottawa, Ottawa, Ontario, Canada
Venue:
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
Year:
2005

Citing 2
Cited 0

Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hierarchical Approach to Robust Background Subtraction using Color and Gradient Information

MOTION '02 Proceedings of the Workshop on Motion and Video Computing

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Abstract

Numerous methods are currently available for motion detection using background modeling and subtraction. However, there are still many challenges to take into account such as moving shadows, illumination changes, moving background, relocation of background objects, and initialization with moving objects. This paper provides a new background subtraction algorithm that aggregates the classification results of several foreground extraction techniques based on UV color deviations, probabilistic gradient information and vector deviations, in order to produce a single decision that is more robust to those challenges.