Monocular Real-Time Foreground Cut Based on Multiple Cues
This paper proposes background segmentation methods that integrate color, lbp contrast and motion cues so as to improve the segmentation results. The background color models based on GMMs are developed to describe the scene. Motion cues is presented to detect the legitimate moved foreground pixels combining with the background subtraction. Lbp contrast features are used to calculate the image contrast robustly. A dynamic binary graph cut is used to implement the final foreground/background segmentation. We valid our method on indoor videos and test it on the benchmark video. Experiment demonstrates our method’s effectiveness.