Abstract: 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 by “foreground” and “non-foreground” label, and moving object detection with shadow elimination is completed. Experiments show that our approach achieves excellent performance.