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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    Title :

    Estimating 3D body pose using uncalibrated cameras


    Contributors:
    Rosales, R. (author) / Siddiqui, M. (author) / Alon, J. (author) / Sclaroff, S. (author)


    Publication date :

    2001-01-01


    Size :

    796471 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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