Human motion segmentation using active shape models

Abstract

Human motion analysis from images is meticulously related to the development of computational techniques capable of automatically identifying, tracking and analyzing relevant structures of the body. This work explores the identification of such structures in images, which is the first step of any computational system designed to analyze human motion. A widely used database (CASIA Gait Database) was used to build a Point Distribution Model (PDM) of the structure of the human body. The training dataset was composed of 14 subjects walking in four directions, and each shape was represented by a set of 113 labelled landmark points. These points were composed of 100 contour points automatically extracted from the silhouette combined with an additional 13 anatomical points from elbows, knees and feet manually annotated. The PDM was later used in the construction of an Active Shape Model, which combines the shape model with gray level profiles, in order to segment the modelled human body in new images. The experiments with this segmentation technique revealed very encouraging results as it was able to gather the necessary data of subjects walking in different directions using just one segmentation model.