Unsupervised NN approach and PCA for background-foreground video segmentation

Abstract

MPEG-4 based video coding applications require the segmentation of each video image in its principal moving objects to be coded independently from each other. Several techniques of video objects segmentation for coding purposes have been presented in literature; all such segmentation techniques are based on the smart soft-thresholding of the motion fields, the best ones dealing with dense motion fields. Anyway, MPEG-4 based coding structures require a block based (sparse) motion field estimation. The use of block based coding structures, doesn't allow fair video objects segmentation for the intrinsic inaccuracy of motion estimate of the block based structure of the motion field, specially on moving object border blocks. In this context the segmentation obtained based only on motion information is inaccurate, but it can be enhanced by the joint use of information at hand, like color, motion, frame difference, prediction error, texture and so on. In this work a locally connected unsupervised neural network approach is presented, to obtain the segmentation of a moving video object (VO) on a fixed or slow-translating background.