Differential Evolution Based Human Body Pose Estimation from Point Clouds

This paper describes a method to estimate the body pose of a human from the point cloud obtained from a depth sensor. It uses Differential Evolution to find the best match between a candidate pose, represented by an instance of a 42-parameter articulated model of a human, and the point cloud. The results, compared to other four state-of-the art methods on a publicly available dataset, show that the method has good ability to estimate the pose of a person and to track him in video sequences. The entire method, from Differential Evolution to fitness computation, is run on nVIDIA GPUs. Thanks to its massively parallel implementation in CUDA-C, it produces pose estimates in real time.