Efficient 2D human pose estimation using mean-shift

Abstract

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.