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

In this paper, a novel background model initiation and maintenance algorithm and shadow suppression scheme by exploiting HSV color information for indoor and outdoor people detection is proposed. In order to obtain the initial background scene, the frequency ratios of the RGB components values for each pixel at the same position in the learning sequence are respectively calculated; the RGB components values with the biggest ratios are incorporated to model the initial background scene. The background maintenance so called background re-initiation is also proposed to adapt to the scene changes, such as illumination changes and scene geometry changes. Moving cast shadows mostly exhibit a challenge for accurate moving targets 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 paper by exploiting HSV color information. The experimental results are given respectively and show the performance of our algorithm.