A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Introduction <s> We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compression to allow larger training sets, and a comparison of several decision-tree training objectives. Key aspects of our work include: regression directly from the raw depth image, without the use of an arbitrary intermediate representation; applicability to general motions (not constrained to particular activities) and the ability to localize occluded as well as visible body joints. Experimental results demonstrate that our method produces state of the art results on several data sets including the challenging MSRC-5000 pose estimation test set, at a speed of about 200 frames per second. Results on silhouettes suggest broader applicability to other imaging modalities. <s> BIB001 </s> A Survey of Efficient Regression of General-Activity Human Poses from Depth Images <s> Introduction <s> Thank you very much for downloading decision forests for computer vision and medical image analysis. As you may know, people have search hundreds times for their favorite readings like this decision forests for computer vision and medical image analysis, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they cope with some malicious bugs inside their computer. <s> BIB002
Human pose estimation from images has been studied for decades in computer vision. As recent development in cameras and sensors, depth images receive a wide spread of notice from researchers from body pose estimation 1 to 3D reconstruction 2 . Girshick et al. BIB001 present an approach to find the joints position in human body from depth images. They address the problem of general-activity pose estimation. Their regression-based approach sucessfully computes the joint positions even with occlusion. Their method can be view as a new combination of two existing works, implicit shape models  and Hough forest BIB002 . The following sections cover related works, explanation on the method from testing to training, and result and comparison. 