Real-time video object segmentation using HSV space

A new method is proposed for real-time video object segmentation from a static background under shadows and varying lighting. The method first learns a statistical model and a type model for the background scene in HSV (hue, saturation and value) space. The latter one represents the reflection characteristic of the object surface in the background and helps decide whether the hue or saturation component should be used to detect changes in the statistical model. At the change detection stage, a multi-criteria decision is taken in order to deal with various light conditions such as shadows. Lastly, a speedup morphologic filter and a connected components labeling algorithm refine the segmentation results. The method makes full and better use of color information in images. The capability of accurate segmentation and real-time execution is demonstrated for several test and practical video sequences.