Background Modeling and Subtraction of Dynamic Scenes

Background modeling and subtraction is a core component in motion analysis. The central idea behind such module is to create a probabilistic representation of the static scene that is compared with the current input to perform subtraction. Such approach is efficient when the scene to be modeled refers to a static structure with limited perturbation. In this paper, we address the problem of modeling dynamic scenes where the assumption of a static background is not valid. Waving trees, beaches, escalators, natural scenes with rain or snow are examples. Inspired by the work proposed in [4], we propose an on-line auto-regressive model to capture and predict the behavior of such scenes. Towards detection of events we introduce a new metric that is based on a state-driven comparison between the prediction and the actual frame. Promising results demonstrate the potentials of the proposed framework.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Antoine Monnet, Anurag Mittal, Nikos Paragios, Visvanathan Ramesh
• Keywords: Computer Vision, Dynamic Scenes, ICCV 2003, Natural Scenes, Static Background, Static Scene, Static Structure