Background Reconstruction Using DWT and Grayscale Classification

Background reconstruction is very important in many video-based tracking systems. The principle difficulties are the quality and velocity of reconstruction. To cope with these problems, a novel method is proposed. Firstly, the sequence images are decomposed into low frequency sub-images using DWT (discrete wavelet transform). Then, the improved grayscale classification is introduced to reconstruct initial background with the latest N frame sub-images. Finally, the background is updated with selective update and background adjustment. Since sub-images are with low-resolution, the reconstruction cost is decreased. With the accumulation sum being introduced to classify grayscales, the background noise is reduced. The experimental results show that the proposed method is efficient.