A transform domain approach to real-time foreground segmentation in video sequences

Accurate foreground segmentation is a difficult task due to factors such as illumination variation, occlusion, background movement, and noise. We present a novel adaptive transform domain approach for foreground segmentation in video sequences. A set of DCT-based features is employed to exploit the spatial and temporal correlation in the video sequences. We maintain an adaptive background model and make a decision based on the distance between the features of the current frame and that of the background model. Additional higher level processing is employed to deal with the variation of the environment and to improve the accuracy of segmentation. The approach is shown to be insensitive to illumination change and to noise. It also overcomes many common difficulties of segmentation such as foreground aperture and moved background objects. The algorithm can perform in real-time.