Abstract A new 2-step method is presented for human upper-body pose estimation from depth sequences, in which coarse human part labeling takes place first, followed by more precise joint position estimation as the second phase. In the first step, a number of constraints are extracted from notable image features such as the head and torso. The problem of pose estimation is cast as that of label assignment with these constraints. Major parts of the human upper body are labeled by this process. The second step estimates joint positions optimally based on kinematic constraints using dense correspondences between depth profile and human model parts. The proposed framework is shown to overcome some issues of existing approaches for human pose tracking using similar types of data streams. Performance comparison with motion capture data is presented to demonstrate the accuracy of our approach.
Constrained Optimization for Human Pose Estimation from Depth Sequences
Computer Vision – ACCV 2007 ; 11 ; 408-418
Lecture Notes in Computer Science ; 4843 , 11
2007-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
Inverse Kinematic , Depth Sequence , Joint Position , Depth Image , Motion Capture Pattern Recognition , Algorithm Analysis and Problem Complexity , Computer Science , Artificial Intelligence (incl. Robotics) , Biometrics , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics
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