1 research outputs found
CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
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.Comment: Accepted in ICPR-201