CANDID：动态背景减除的鲁棒变化动力学和确定性更新策略
CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction
摘要：
视频中的背景减除提供了许多计算机视觉应用所必需的初步信息。在本文中，我们提出了一系列的方法命名为CANDID处理具有挑战性的视频场景中的变化检测问题。CANDID自适应地调整像素级距离阈值和更新速率。这些参数通过计算某个位置的变化动态来更新。此外，通过制定确定性更新策略来维护背景模型。所提出的方法的性能进行评估，在各种具有挑战性的情况下，如动态背景和极端天气条件。所提出的方法的定性和定量措施优于现有的国家的最先进的方法。
英文摘要：
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.
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