A Statistical Background Modeling Algorithm for Real-Time Pixel Classification

Authors

  • Ricardo Acevedo-Avila Tecnológico de Monterrey, Campus Estado de México.
  • Miguel Gonzalez-Mendoza Tecnológico de Monterrey, Campus Estado de México.
  • Andres Garcia-Garcia Tecnológico de Monterrey, Campus Estado de México.

DOI:

https://doi.org/10.13053/cys-22-3-2554

Keywords:

Background Modeling, Embedded Computer Vision, Statistical Pixel Modeling, Image Processing, Object Detection

Abstract

This paper introduces a statistical background pixel classifier intended for real-time and low-resource implementation. The algorithm works within a smart video surveillance application aimed to detect unattended objects in images with fixed backgrounds. The algorithm receives an input image and builds an initial background model based on image statistics. Using this information, the algorithm identifies new objects that do not belong to the original image. The algorithm categorizes image pixels in four possible classes: shadows, midtones, highlights and foreground pixels. The classification stage produces a binary mask where only objects of interest are shown.The pixel classifier processes Quarter VGA (320 x 240) gray-scale images at a nomial processing rate of 30 frames per second. Higher resolutions such as VGA (640 x 480) have been also tested. We compare results with traditional statistical background modeling methods. Our experiments demonstrate that our approach achieves successful background segmentation at a minimal resource consumption while maintaining real-time execution.

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Published

2018-09-26

Issue

Vol. 22 No. 3 (2018): Thematic issue on Natural Language Processing (Guest Editor: A. Gelbukh)

Section

Articles

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