Abstract:
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.
