Contents 1. INTRODUCTION
In surveillance applications, the camera has typically a fixed position and for security reasons, system users are particularly interested in the moving regions in the video signals differing from the (possible) camera motion. Background (BG) subtraction is used to segment moving regions in image sequences, by comparing each new frame to a model of the scene BG. In this paper, we present an efficient nonparametric BG model and a BG-subtraction algorithm that require only the luminance channel of a heavily downsampled video. The model has three advantages. First, the model can handle complex situations where the BG of the scene is cluttered and not completely static but contains small motions, such as found in tree branches and leaves. Second, the model can also extrapolate luminance values to estimate pixel trends and incorporate medium-speed changes, and it adapts quickly to scene changes to facilitate a very sensitive detection of moving targets. Third, it learns objects that are detected as foreground (FG) for a long time and we propose a solution for learning the BG objects starting to move after an initial BG model was created. At the same time, we found a way to reduce memory size significantly.
