Exploiting Color and Depth for Background Subtraction

Abstract

Background subtraction from color and depth data is a fundamental task for indoor video surveillance applications that use data acquired by RGBD sensors. This paper proposes a method based on two background models for color and depth information, exploiting a self-organizing neural background model previously adopted for RGB videos. The resulting color and depth detection masks are combined, not only to achieve the final results, but also to better guide the selective model update procedure. The experimental evaluation on the SBM-RGBD dataset shows that the exploitation of depth information allows to achieve much higher performance than just using color, accurately handling color and depth background maintenance challenges.