Unsupervised Construction of Human Body Models Using Principles of Organic Computing
This work addresses the need for computing systems to interact naturally with humans by improving autonomy in behavior understanding, though it appears incremental in integrating existing principles.
The paper tackles the problem of unsupervised learning of generalizable human body models from video data, achieving models that show good generalization to different individuals, backgrounds, and attire, enabling robust interpretation of single video frames and posture mimicking by an android robot.
Unsupervised learning of a generalizable model of the visual appearance of humans from video data is of major importance for computing systems interacting naturally with their users and others. We propose a step towards automatic behavior understanding by integrating principles of Organic Computing into the posture estimation cycle, thereby relegating the need for human intervention while simultaneously raising the level of system autonomy. The system extracts coherent motion from moving upper bodies and autonomously decides about limbs and their possible spatial relationships. The models from many videos are integrated into meta-models, which show good generalization to different individuals, backgrounds, and attire. These models allow robust interpretation of single video frames without temporal continuity and posture mimicking by an android robot.