Human pose estimation is the process of approximating the configuration of the body's underlying skeletal articulation in one or more frames. The curve-skeleton of an object is a line-like representation that preserves topology and geometrical information. Finding the curve-skeleton of a volume corresponding to the person is a good starting point for approximating the underlying skeletal structure. In this paper a GPU implementation of a fully parallel thinning algorithm based on the critical kernels framework is presented. The algorithm is compared to another state-of-the-art thinning method, and while it is demonstrated that both achieve real-time frame rates, the proposed algorithm yields superior accuracy and robustness when used in a pose estimation context. The GPU implementation is > 8× faster than a sequential version, and the positions of the four extremities are estimated with rms error ∼ 6 cm and ∼ 98 % of frames correctly labelled.

Human pose estimation is the process of approximating the configuration of the body’s underlying skeletal articulation in one or more frames. The curve-skeleton of an object is a line-like representation that preserves topology and geometrical information. Finding the curve-skeleton of a volume corresponding to the person is a good starting point for approximating the underlying skeletal structure. In this paper, a GPU implementation of a fully parallel thinning algorithm based on the critical kernel framework is presented. The algorithm is compared to three other state-of-the-art skeletonisation methods—two CPU and one GPU implementation—using both real and synthetic data. It is demonstrated that all four achieve close to real-time frame rates, however, the proposed algorithm yields superior accuracy and robustness when used in a pose estimation context. The GPU implementation is > 8× faster than a CPU implementation of the same algorithm, and the positions of the 4 extremities are estimated with rms error ∼6 cm and ∼98% of frames correctly labelled for some sequences.