Putting the pieces together: Connected Poselets for human pose estimation

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Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, methodology): “Many other datasets captured using a Kinect v1 camera were also released to the public, including the MSR Daily Activity 3D [121], MSR Action Pairs [113], Online RGBD Action (ORGBD) [107], UTKinect-Action [124], Florence 3D-Action [118], CMU-MAD [104], UTD-MHAD [103], G3D/G3Di [105], [119], SPHERE [”
Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images (2014, background): “…reveals that our approach achieves a body part detection accuracy on a par with the state-of-the-art on the sports dataset and greatly outperforms [5] in terms of joint localization accuracy on the FashionPose dataset, while achieving a run time of a couple of images per second on the LSP dataset.”
Human Pose Estimation Using Body Parts Dependent Joint Regressors (2013, methodology): “Random forests have been also used to improve poselets for pose estimation from depth data [16] and for pedestrian detection [27].”