Posture recognition invariant to background, cloth textures, body size, and camera distance using morphological geometry

The human posture estimation in surveillance caring application can improves the people everyday life, In this paper, we propose a method that is invariant to background, distance of camera location, size and cloths of people in the frames. A silhouette is projected to the horizontal and vertical histograms for features extraction. The important features are based on the length and width of body parts of human. The proposed features are more suitable for classifying human posture into four main categories such as standing, lying, sitting, and bending, obviously appeared with the high percentage of recognition when compared with the traditional features in the ANFIS model. The increase of accuracy comes from the robustness of various environments such as the complicated posture of a changed body position and camera distance.