Foreground Segmentation in Videos Combining General Gaussian Mixture Modeling and Spatial Information

We present a new statistical approach combining temporal and spatial information for robust online background subtraction (BS) in videos. Temporal information is modeled by coupling finite mixtures of generalized Gaussian distributions with foreground/background co-occurrence analysis. Spatial infor...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2018-06, Vol.28 (6), p.1330-1345
Main Authors: Boulmerka, Aissa, Allili, Mohand Said
Format: Article
Language:English
Subjects:
Background subtraction (BS)
Cameras
cast shadows
co-occurrence/correlation analysis (CA)
Computational modeling
Computing time
Correlation analysis
dynamic backgrounds
Fuses
Heuristic algorithms
Hidden Markov models
Histograms
Jitter
Lighting
mixture models
Multiscale analysis
pan-tilt-zoom (PTZ)
Segmentation
Spatial data
Standard data
State of the art
Subtraction
temporal/spatial information
Videos
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Description
Summary:We present a new statistical approach combining temporal and spatial information for robust online background subtraction (BS) in videos. Temporal information is modeled by coupling finite mixtures of generalized Gaussian distributions with foreground/background co-occurrence analysis. Spatial information is modeled by combining multiscale inter-frame correlation analysis and histogram matching. We propose an online algorithm that efficiently fuses both information to cope with several BS challenges, such as cast shadows, illumination changes, and various complex background dynamics. In addition, global video information is used through a displacement measuring technique to deal with pan-tilt-zoom camera effects. Experiments with comparison with recent state-of-the-art methods have been conducted on standard data sets. Obtained results have shown that our approach surpasses several state-of-the-art methods on the aforementioned challenges while maintaining comparable computational time.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2017.2665970