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

Abstract

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 (MoGG) distributions with foreground/background co-occurrence analysis. Spatial information is modeled by combining multi-scale 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-tiltzoom (PTZ) camera effects. Experiments with comparison with recent state-of-the-art methods have been conducted on standard datasets. Obtained results have shown that our approach surpasses several state-of-the-art methods on the aforementioned challenges while maintaining comparable computational time. Index Terms—Background subtraction (BS), temporal/spatial information, mixture models, co-occurrence/correlation analysis, cast shadows, dynamic backgrounds, pan-tilt-zoom (PTZ).