Adaptive Weight Selection for Incremental Eigen-Background Modeling

Background modeling is an important approach for motion detection. The background model should adapt to dynamic change of the environment in time and generate background image with no moving foreground. Accordingly, we propose to incorporate the adaptive weight selection mechanism for roughly detected motion regions into the incremental eigen-background method. Comparing with existing works, we originally provide a way to reasonably design and adaptively compute the weight for each frame. Experiments show that the proposed adaptive incremental eigen-background method not only models the dynamic background scene well but also generates better background image with no ghost effect when salient motion occurs.