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Robust Background Subtraction with Shadow and Highlight Removal for Indoor Surveillance

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 082931 (2007) Cite this article

Abstract

This work describes a robust background subtraction scheme involving shadow and highlight removal for indoor environmental surveillance. Foreground regions can be precisely extracted by the proposed scheme despite illumination variations and dynamic background. The Gaussian mixture model (GMM) is applied to construct a color-based probabilistic background model (CBM). Based on CBM, the short-term color-based background model (STCBM) and the long-term color-based background model (LTCBM) can be extracted and applied to build the gradient-based version of the probabilistic background model (GBM). Furthermore, a new dynamic cone-shape boundary in the RGB color space, called a cone-shape illumination model (CSIM), is proposed to distinguish pixels among shadow, highlight, and foreground. A novel scheme combining the CBM, GBM, and CSIM is proposed to determine the background which can be used to detect abnormal conditions. The effectiveness of the proposed method is demonstrated via experiments with several video clips collected in a complex indoor environment.

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Authors and Affiliations

  1. Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, 300, Taiwan

    Jwu-Sheng Hu & Tzung-Min Su

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  1. Jwu-Sheng Hu
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  2. Tzung-Min Su
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Correspondence to Jwu-Sheng Hu.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Hu, JS., Su, TM. Robust Background Subtraction with Shadow and Highlight Removal for Indoor Surveillance. EURASIP J. Adv. Signal Process. 2007, 082931 (2007). https://doi.org/10.1155/2007/82931

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