Automatic acquisition and initialization of articulated models

Tracking, classification and visual analysis of articulated motion is challenging because of the difficulties involved in separating noise and variabilities caused by appearance, size and viewpoint fluctuations from task-relevant variations. By incorporating powerful domain knowledge, model-based approaches are able to overcome these problem to a great extent and are actively explored by many researchers. However, model acquisition, initialization and adaptation are still relatively under-investigated problems, especially for the case of single-camera systems. In this paper, we address the problem of automatic acquisition and initialization of articulated models from monocular video without any prior knowledge of shape and kinematic structure. The framework is applied in a human-computer interaction context where articulated shape models have to be acquired from unknown users for subsequent limb tracking. Bayesian motion segmentation is used to extract and initialize articulated models from visual data. Image sequences are decomposed into rigid components that can undergo parametric motion. The relative motion of these components is used to obtain joint information. The resulting components are assembled into an articulated kinematic model which is then used for visual tracking, eliminating the need for manual initialization or adaptation. The efficacy of the method is demonstrated on synthetic as well as natural image sequences. The accuracy of the joint estimation stage is verified on ground truth data.