|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
HIERARCHICAL PART-BASED HUMAN BODY POSE ESTIMATION
R. Navaratnam , A. Thayananthan , P. H. S. Torr , R. Cipolla
This paper addresses the problem of automatic detection and recovery of three-dimensional human body pose from monocular video sequences for HCI applications. We propose a new hierarchical part-based pose estimation method for the upper-body that efficiently searches the high dimensional articulation space. The body is treated as a collection of parts linked in a kinematic structure. Search for configurations of this collection is commenced from the most reliably detectable part. The rest of the parts are searched based on the detected locations of this anchor as they all are kinematically linked. Each part is represented by a set of 2D templates created from a 3D model, hence inherently encoding the 3D joint angles. The tree data structure is exploited to efficiently search through these templates. Multiple hypotheses are computed for each frame. By modelling these with a HMM, temporal coherence of body motion is exploited to find a smooth trajectory of articulation between frames using a modified Viterbi algorithm. Experimental results show that the proposed technique produces good estimates of the human 3D pose on a range of test videos in a cluttered environment.
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