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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Bariko, Smail
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Economics, Econometrics & Finance

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Improved automated parallel implementation of GMM background subtraction on a multicore digital signal processor Bariko, Smail; Klilou, Abdessamad; Abounada, Abdelouahed; Arsalane, Assia
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp552-559

Abstract

Scene segmentation is an essential step in a wide range of video processing applications, for instance, object recognition and tracking. The Gaussian mixture model (GMM) for background subtraction (BS) has gained widespread usage in scene segmentation, despite its known computational intensity. To tackle this challenge, we propose a practical solution to accelerate processing through a parallel implementation on an embedded multicore platform. In this paper, we present an improved automated parallel implementation of the GMM algorithm using the Orphan directive provided by open multiprocessing (OpenMP). Experimental assessments conducted on the eight cores of the C6678 digital signal processor (DSP) demonstrate significant advancements in parallel efficiency, particularly when handling high-resolution frames, including high-definition (HD) and full-HD resolutions. The achieved parallel efficiency surpasses the results obtained with classical OpenMP scheduling modes, encompassing dynamic, static, and guided approaches. Specifically, the parallel efficiency reaches approximately 82% for full-HD resolution frames and, 99.3% for low-resolution frames, respectively.
Co-Authors Abounada, Abdelouahed Arsalane, Assia Klilou, Abdessamad