A Survey of Efficient Regression of General-Activity Human Poses from Depth Images
This is an incremental survey paper for researchers in computer vision and human-computer interaction.
This paper provides a comprehensive review of regression-based methods for human pose estimation from depth images, analyzing state-of-the-art approaches and their experimental results across different scenarios.
This paper presents a comprehensive review on regression-based method for human pose estimation. The problem of human pose estimation has been intensively studied and enabled many application from entertainment to training. Traditional methods often rely on color image only which cannot completely ambiguity of joint 3D position, especially in the complex context. With the popularity of depth sensors, the precision of 3D estimation has significant improvement. In this paper, we give a detailed analysis of state-of-the-art on human pose estimation, including depth image based and RGB-D based approaches. The experimental results demonstrate their advantages and limitation for different scenarios.