No abstract

A model-based approach to reconstruction of 3D human arm motion from a monocular image sequence taken under orthographic projection is presented. The reconstruction is divided into two stages. First, a 2D shape model is used to track the arm silhouettes and second-order curves are used to model the arm based on an iteratively reweighted least square method. As a result, 2D stick figures are extracted. In the second stage, the stick figures are backprojected into the scene. 3D postures are reconstructed using the constraints of a 3D kinematic model of the human arm. The motion of the arm is then derived as a transition between the arm postures. Applications of these results are foreseen in the analysis of human motion patterns.

The conventional least squared distance method of fitting a model t o a set of data points gives unreliable results when the amount of noise in the input is significani compared with the amount of data correlated io the model itself. The iheory of robust staiistics formally addresses these problems and is used in this work io develop a method of separation of ihe data of interest from noise. It is based on iteraiively reweighied least squares algorithm where Hampel redescending function is applied f o r weighting data. The method has been eficienily tested in modeling synthetic and real ZD image data with second order curves.