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    • CAS: Computer Science: Technical Reports
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    •   OpenBU
    • College of Arts and Sciences
    • Computer Science
    • CAS: Computer Science: Technical Reports
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    Estimating 3D Body Pose Using Uncalibrated Cameras

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    Date Issued
    2001-06
    Author(s)
    Rosales, Rómer
    Siddiqui, Matheen
    Alon, Jonathan
    Sclaroff, Stan
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    Permanent Link
    https://hdl.handle.net/2144/1629
    Abstract
    An approach for estimating 3D body pose from multiple, uncalibrated views is proposed. First, a mapping from image features to 2D body joint locations is computed using a statistical framework that yields a set of several body pose hypotheses. The concept of a "virtual camera" is introduced that makes this mapping invariant to translation, image-plane rotation, and scaling of the input. As a consequence, the calibration matrices (intrinsics) of the virtual cameras can be considered completely known, and their poses are known up to a single angular displacement parameter. Given pose hypotheses obtained in the multiple virtual camera views, the recovery of 3D body pose and camera relative orientations is formulated as a stochastic optimization problem. An Expectation-Maximization algorithm is derived that can obtain the locally most likely (self-consistent) combination of body pose hypotheses. Performance of the approach is evaluated with synthetic sequences as well as real video sequences of human motion.
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    • CAS: Computer Science: Technical Reports [584]


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