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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 making the posture estimation cycle more autonomous. The system extracts coherent motion from moving upper bodies and autonomously decides about...
This paper addresses the problem of upper body pose estimation. The task is to detect and estimate 2D human configuration in static images for six parts: head, torso, and left-right upper and lower arms. The common approach to solve this has been the Pictorial Structure method (Felzenszwalb and Huttenlocher, 2005). We present this as a graphical model inference problem and use the loopy belief propagation...
We introduce a vision based, marker less upper body pose tracking approach that first tracks the 3D movements of extremities, including head and hands. Then based on the knowledge of upper body model, these extremity movements are used to predict the whole upper body motion as an inverse kinematics problem. The experimental validation showed the promise of applying this approach in several smart environments...
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