ArticleSensors (Basel, Switzerland)2022
Markerless 3D Skeleton Tracking Algorithm by Merging Multiple Inaccurate Skeleton Data from Multiple RGB-D Sensors.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The ADVANCE toolkit: Automated descriptive video annotation in naturalistic child environments.Behavior research methods · 2025Article
- GAIT-CKD (Gait Analysis using Artificial Intelligence for digital Therapeutics of patients with Chronic Kidney Disease): design and methods.Kidney research and clinical practice · 2025Article
- Reliability and Validity of the Single-Camera Markerless Motion Capture System for Measuring Shoulder Range of Motion in Healthy Individuals and Patients with Adhesive Capsulitis: A Single-Center Study.Sensors (Basel, Switzerland) · 2025Article
- Objective gait analysis using Kinect v2® for the prognosis of walking during the acute phase of rehabilitation after proximal femoral fracture surgery.Frontiers in rehabilitation sciences · 2025Article
- Human Motion Enhancement and Restoration via Unconstrained Human Structure Learning.Sensors (Basel, Switzerland) · 2024Article
- Simulation of Human Movement in Zero Gravity.Sensors (Basel, Switzerland) · 2024Article
- A Review of Depth-Based Human Motion Enhancement: Past and Present.IEEE journal of biomedical and health informatics · 2024Review
- On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning-Based ADHD and ADHD-RISK Classification Technology through the Recognition of Children's Abnormal Behaviors during the Robot-Led ADHD Screening Game.Sensors (Basel, Switzerland) · 2022Article
- Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.Sensors (Basel, Switzerland) · 2022Article
- Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Skeleton data, which is often used in the HCI field, is a data structure that can efficiently express human poses and gestures because it consists of 3D positions of joints. The advancement of RGB-D sensors, such as Kinect sensors, enabled the easy capture of skeleton data from depth or RGB images. However, when tracking a target with a single sensor, there is an occlusion problem causing the quality of invisible joints to be randomly degraded. As a result, multiple sensors should be used to reliably track a target in all directions over a wide range. In this paper, we proposed a new method for combining multiple inaccurate skeleton data sets obtained from multiple sensors that capture a target from different angles into a single accurate skeleton data. The proposed algorithm uses density-based spatial clustering of applications with noise (DBSCAN) to prevent noise-added inaccurate joint candidates from participating in the merging process. After merging with the inlier candidates, we used Kalman filter to denoise the tremble error of the joint's movement. We evaluated the proposed algorithm's performance using the best view as the ground truth. In addition, the results of different sizes for the DBSCAN searching area were analyzed. By applying the proposed algorithm, the joint position accuracy of the merged skeleton improved as the number of sensors increased. Furthermore, highest performance was shown when the searching area of DBSCAN was 10 cm.
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