Mr. Firas Achkar Profile

Firas Achkar

at Concordia Univ

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Proceedings Article | 29 January 2007 Paper

Hysteresis-based selective Gaussian-mixture model for real-time background update

Firas Achkar, Aishy Amer

Proceedings Volume 6508, 65082J (2007) https://doi.org/10.1117/12.704717

KEYWORDS: Video, RGB color model, Fiber optic gyroscopes, Blob detection, Video processing, Cameras, Video surveillance, Data modeling, Motion models, Computer simulations

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We propose a novel Mixture of Gaussian (MOG)-based real-time background update technique. The proposed technique consists of a new selective matching scheme based on the combined approaches of component ordering and winner-takes-all. This matching scheme not only selects the most probable component for the first matching with new pixel data, greatly improving performance, but also simplifies pixel classification and component replacement in case of no match. Further performance improvement achieved by using a new simple and functional component variance adaptation formula. Also in this technique, the proposed new hysteresis-based component matching and temporal motion history schemes improve segmentation quality. Component hysteresis matching improves detected foreground object blobs by reducing the amount of cracks and added shadows, while motion history preserves the integrity of moving objects boundaries, both with minimum computational overhead. The proposed background update technique implicitly handles both gradual illumination change and temporal clutter problems. The problem of shadows and ghosts is partially addressed by the proposed hysteresis-based matching scheme. The problem of persistent sudden illumination changes and camera movements are addressed at frame level depending on the percentage of pixels classified as foreground. We implemented three different state-of-the-art background update techniques and compared their segmentation quality and computational performance with those of the proposed technique. Experimental results on reference outdoor sequences and real traffic surveillance streams show that the proposed technique improved segmentation accuracy for extracting moving objects of interest compared to other reference techniques.

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