Robust background subtraction with shadow and highlight removal for indoor surveillance

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.