Sentence examples for human pose estimation from inspiring English sources

The paper by Wang et al., entitled "A two-stage bayesian network method for 3D human pose estimation from monocular image sequences," designs a 3D pose estimator that uses a two-stage inference hierarchy.

Human pose estimation is a key step to action recognition.

In this paper we consider the problem of human pose estimation from a single still image.

Articulated human pose estimation in unconstrained conditions is a great challenge.

We introduce a hierarchical part-based approach for human pose estimation in static images.

It first gives an overview of various deep learning approaches and their recent developments, and then briefly describes their applications in diverse vision tasks, such as image classification, object detection, image retrieval, semantic segmentation and human pose estimation.

In this paper we address the problem of human body pose estimation from still images.

A bottom-up parsing approach can be used to recognize the human body for performing pose estimation by segmenting multiple images.

The accepted papers re?ect the state of the art in the ?eld and cover various topicsrelatedto humanmotiontrackingandanalysis.Thepapersinthisvolume have been classi ed into three categories based on the topics they cover: human motion capture and pose estimation, body and limb tracking and segmentation, and activity recognition.

In terms of the image processing for motion capture, the measured depth can be used for the building of 3D human models through 2D pose estimation (i.e., 2D skeletons with depth), as well as for the direct inference of 3D poses by integrating the depth into the pose estimation process.

The pose estimation process estimates the camera poses based on the matched feature points from previous procedures.