Indoor and outdoor people detection and shadow suppression by exploiting HSV color information

An adaptive background model based on maximum statistical probability and a shadow suppression scheme for indoor and outdoor people detection by exploiting hue saturation value (HSV) color information is proposed. To obtain the initial background scene, the frequency of R, G, and B component values for each pixel at the same position in the learning sequence are respectively calculated; the R, G, and B component values with the biggest ratios are incorporated to model the initial background. The background maintenance, or the socalled background re-initiation, is also proposed to adapt to scene changes such as illumination changes and scene geometry changes. Moving cast shadows generally exhibit a challenge for accurate moving target detection. Based on the observation that a shadow cast on a background region lowers its brightness but does not change its chromaticity significantly, we address this problem in the article by exploiting HSV color information. In addition, quantitative metrics is introduced to evaluate the algorithm on a benchmark suite of indoor and outdoor video sequences. The experimental results are given to show the performance of the algorithm.