Adaptive video background modeling using color and depth

A new algorithm for background estimation and removal in video sequences obtained with stereo cameras is presented. Per-pixel Gaussian mixtures are used to model recent scene observations in the combined space of depth and luminance-invariant color. These mixture models adapt over time, and are used to build a new model of the background at each time step. This combination in itself is novel, but we also introduce the idea of modulating the learning rate of the background model according to the scene activity level on a per-pixel basis, so that dynamic foreground objects are incorporated into the background more slowly than are static scene changes. Our results show much greater robustness than prior state-of-the-art methods to challenging phenomena such as video displays, non-static background objects, areas of high foreground traffic, and similar color of foreground and background. Our method is also well-suited for use in real-time systems