3D Joints Estimation of the Human Body in Single-Frame Point Cloud

Xu, Tianxu; An, Dong; Wang, Zhonghan; Jiang, Sicheng; Meng, Chengnuo; Zhang, Yiwen; Wang, Qiang; Pan, Zhongqi; Yue, Yang

doi:10.1109/access.2020.3027892

Cited by 9 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xu et al [ 57 ] detected the human joints in a single-frame point cloud using the TOF depth camera. The process was distributed into three stages as shown in Figure 9 .…”

Section: Methods Of Point Cloud-based Joint Estimationmentioning

confidence: 99%

See 1 more Smart Citation

A Review: Point Cloud-Based 3D Human Joints Estimation

Jia

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Joint estimation of the human body is suitable for many fields such as human–computer interaction, autonomous driving, video analysis and virtual reality. Although many depth-based researches have been classified and generalized in previous review or survey papers, the point cloud-based pose estimation of human body is still difficult due to the disorder and rotation invariance of the point cloud. In this review, we summarize the recent development on the point cloud-based pose estimation of the human body. The existing works are divided into three categories based on their working principles, including template-based method, feature-based method and machine learning-based method. Especially, the significant works are highlighted with a detailed introduction to analyze their characteristics and limitations. The widely used datasets in the field are summarized, and quantitative comparisons are provided for the representative methods. Moreover, this review helps further understand the pertinent applications in many frontier research directions. Finally, we conclude the challenges involved and problems to be solved in future researches.

show abstract

“…Xu et al [ 57 ] detected the human joints in a single-frame point cloud using the TOF depth camera. The process was distributed into three stages as shown in Figure 9 .…”

Section: Methods Of Point Cloud-based Joint Estimationmentioning

confidence: 99%

“…( c ) Fourteen joints of human body are extracted by using the geometric feature of human silhouette. Figure from [ 57 ].…”

Section: Figurementioning

confidence: 99%

A Review: Point Cloud-Based 3D Human Joints Estimation

Jia

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…According to the disparity map, the coordinate information corresponding to each pixel in the images can be calculated. The method based on spatial points is beneficial to realize rapid 3D point cloud reconstruction of breast model [33]. In the world coordinate system, there is a point P, and its corresponding spatial information is (Xw, Yw, Zw).…”

Section: Reconstruction Of 3d Point Cloud Modelmentioning

confidence: 99%

Scanning Path Planning of the Robot for Breast Ultrasound Examination Based on Binocular Vision and NURBS

Zhang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Ultrasonic scanning is the most commonly used method for detecting breast cancer. Traditional breast ultrasound mainly relies on manual examination, which requires doctors with rich clinical experience. The scanning path planning of the robot for breast ultrasound examination based on binocular vision and non-uniform rational B-Splines (NURBS) is proposed in this paper to solve the limitations of traditional examination methods. First, breast images are acquired by the binocular camera. The depth information of the images is obtained through stereo matching, and the 3D point cloud reconstruction of the breast model is completed. Second, the spatial index algorithm based on the K-dimension tree (Kd-tree) and uniform sampling method are selected to denoise and down-sample the point cloud model to complete the preliminary optimization of the model. The point cloud information for the scanning path is extracted according to the method of point cloud extraction and the principle of normal vector calculation. Then, the interpolation optimization of the scanning path based on the NURBS curve is completed. The optimized scanning path is transformed into the robot motion path. Finally, the robot motion is simulated and analyzed, and the experimental results show that the rationality of robot operation and the smoothness of robot motion meet the requirements of path planning defined in this paper. The error analysis experiment shows that the path planned by this method can ensure the efficiency and accuracy of ultrasound examination.INDEX TERMS Ultrasound examination, binocular vision, NURBS, scanning path planning, point cloud reconstruction, robot path planning.

show abstract

“…Another type of input is derived from motion capture, where information changes are tracked from one image to another. Models of this kind usually define the optical sequence as a vector describing the movement of pixels in series of still images, while so called scene sequence can be tracked in video materials referring to the motion of three-dimensional objects within the scene, relative to the distance from the camera lens [79].…”

Section: ) Hybridmentioning

confidence: 99%

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Al‐Qurishi¹,

Khalid²,

Souissi³

2021

IEEE Access

View full text Add to dashboard Cite

People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential. We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly. Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies. This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers. This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis. In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay. Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment. However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.

show abstract

3D Joints Estimation of the Human Body in Single-Frame Point Cloud

Cited by 9 publications

References 29 publications

A Review: Point Cloud-Based 3D Human Joints Estimation

A Review: Point Cloud-Based 3D Human Joints Estimation

Scanning Path Planning of the Robot for Breast Ultrasound Examination Based on Binocular Vision and NURBS

Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

Contact Info

Product

Resources

About