KDE based Simultaneous Background Model Learning and Entropy based Fusion of Cascaded Features for Video Object Segmentation with Shadow Removal

Object detection with shadow removal is one of the challenging issues in computer vision. Dynamic resembles a moving object’s properties, so separating this from object task. This dynamic if not eliminated distorts shape object. In paper, novel scheme for and proposed based on background modeling fused feature space these models learn to take care scene dynamics. Initially, KDE space, temporal spatial (TMS-KDE) carried out cascaded features Gabor HOG are obtained. Besides, original video frame transformed into YCbCr color LBP extracted. The probabilistically generate frames which used modeling. weights fusion determined by entropy measure. Background model learning pixel approach classified either or foreground during process.We have tested our method wide range datasets includes ATON-CVRR, LASIESTA, CD-net, Kaggle, PETS 2006, SGM-RGBD, SBMI 2015, SBMnet 2016 VIRAT. found different conditions while detecting performance be superior that many existing schemes.