Moving Shadow Elimination Based on Shadow Flow and 3D MAP-MRF
Bo Li, Yuan Bao-zong · Signal Processing · 2011
Elimination of shadow is an important issue in moving object detection.In this paper,we present a novel approach of moving shadow elimination based on Shadow Flow and maximum a posteriori probability of 3D Markov Random Field(3D MAP-MRF). Firstly,Gaussian Mixture Model(GMM)is built as background model of per pixel.By comparing current pixel and GMM,we classify candidate shadow pixel through a shadow weak classifier and send it to Shadow Flow Model.Through on-line learning the candidate shadow which comes from weak classifier,our method get high confidence shadow model.Then,3D MRF is constructed of GMM, Shadow Flow and current images.MAP-MRF/min energy is deviated from moving object detection.Finally 3D graph is constructed according 3D MRF.A dynamic graph cuts algorithm is used to find min-cut/max-flow,which is equal to a maximum posteriori probability of label.Each pixel is assigned byforegroundandnon-foregroundlabel,and moving object detection with shadow elimination is completed.Experiments show that our approach achieves excellent performance.