In 2D pose estimation, each limb is parametrized by it position(2D), scale(1D) and orientation(1D). One of the key bottlenecks is the exhaustive search in this 4D limb space where only a few maxima in the space are desired. To reduce the search space, we reformulate this problem in terms of finding the modes of a likelihood distribution and solve it using the Mean-Shift algorithm. Ours is the first paper in the pose estimation community to use such an approach. In addition, we describe a complete top-down approach that estimates limbs in a sequential pair-wise manner. This allows us to use Kinematic Constraints before processing, requiring us to perform search in only a small sub-region of the image for each limb. We finally devise a PCA based pose validation criteria that enables us to prune invalid hypotheses. Combining these search-space reduction techniques allows our method to generate results at par with the state-of-the-art, while saving more than 80% computations when compared to full image search.
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