Background modeling for segmentation of video-rate stereo sequences

Abstract

Stereo sequences promise to be a powerful method for segmenting images for applications such as tracking human figures. We present a method of statistical background modeling for stereo sequences that improves the reliability and sensitivity of segmentation in the presence of object clutter. The dynamic version of the method, called gated background adaptation, can reliably learn background statistics in the presence of corrupting foreground motion. The method has been used with a simple head discriminator to detect and track people using a stereo head mounted on a pan/tilt platform. It runs at video rates using standard PC hardware.