CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
This addresses background subtraction for computer vision applications, but it appears incremental as it builds on existing methods with adaptive parameter tuning.
The paper tackled the problem of background subtraction in challenging video scenarios by proposing CANDID, a method that adaptively initializes parameters and uses a deterministic update policy, resulting in performance that outperforms existing state-of-the-art approaches in qualitative and quantitative measures.
Background subtraction in video provides the preliminary information which is essential for many computer vision applications. In this paper, we propose a sequence of approaches named CANDID to handle the change detection problem in challenging video scenarios. The CANDID adaptively initializes the pixel-level distance threshold and update rate. These parameters are updated by computing the change dynamics at a location. Further, the background model is maintained by formulating a deterministic update policy. The performance of the proposed method is evaluated over various challenging scenarios such as dynamic background and extreme weather conditions. The qualitative and quantitative measures of the proposed method outperform the existing state-of-the-art approaches.