Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and u...Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces.To employ multiple devices simultaneously,Kinect devices need to be calibrated with respect to each other.There are several schemes available that calibrate 3D images generated frommultiple Kinect devices,including themarker detection method.In this study,we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection,which directly affects calibration accuracy;it offers superior userfriendliness,efficiency,and accuracy.Further,we applied a joint tracking algorithm to approximate the calibration.Traditional methods require the information of multiple joints for calibration;however,Azure Kinect,the latest version of Kinect,requires the information of only one joint.The obtained result was further refined using the iterative closest point algorithm.We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional markerbased calibration.展开更多
Timely and effective knee function evaluation and knee exercises promote the prevention and self-management of knee diseases.In this paper,aKinectbased exergame system is proposed to assess and train the knee function...Timely and effective knee function evaluation and knee exercises promote the prevention and self-management of knee diseases.In this paper,aKinectbased exergame system is proposed to assess and train the knee function.Azure Kinect was used to capture and generate 3D models of the user and immerse them in an interactive virtual environment.The software included three functional modules:knee function evaluation,Knee exercises game,and Comprehensive evaluation.The stand,step,leg lift,and squat were selected for knee function evaluation and exercises.Twenty volunteers participated in the experiment.Intraclass correlation coefficients(ICCs)were calculated to assess the reliability of kinematic measurements of knee angles during the movements.The ICC of these movements were stand(ICC=0.987),step(ICC=0.997),left leg lift(ICC=0.981),right leg lift(ICC=0.990),stand(ICC=0.998).The results show that the test-retest reliability is high.It means that the motion capture data is effective and the data obtained by Kinect is stable.展开更多
This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedes...This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedestrian detection algorithms during training. Furthermore, it is dedicated to addressing occlusion issues in pedestrian detection by using Azure Kinect for body tracking and integrating a robust occlusion management algorithm, significantly enhancing detection efficiency. In experiments, an average latency of 204 milliseconds was measured, and the detection accuracy reached an outstanding level of 97%. Additionally, this approach has been successfully applied in creating a simple yet captivating augmented reality game, demonstrating the practical application of the algorithm.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Korea Government(MSIT)(Grant No.NRF-2022R1A2C1004588).
文摘Reconstructing a three-dimensional(3D)environment is an indispensable technique to make augmented reality and augmented virtuality feasible.A Kinect device is an efficient tool for reconstructing 3D environments,and using multiple Kinect devices enables the enhancement of reconstruction density and expansion of virtual spaces.To employ multiple devices simultaneously,Kinect devices need to be calibrated with respect to each other.There are several schemes available that calibrate 3D images generated frommultiple Kinect devices,including themarker detection method.In this study,we introduce a markerless calibration technique for Azure Kinect devices that avoids the drawbacks of marker detection,which directly affects calibration accuracy;it offers superior userfriendliness,efficiency,and accuracy.Further,we applied a joint tracking algorithm to approximate the calibration.Traditional methods require the information of multiple joints for calibration;however,Azure Kinect,the latest version of Kinect,requires the information of only one joint.The obtained result was further refined using the iterative closest point algorithm.We conducted several experimental tests that confirmed the enhanced efficiency and accuracy of the proposed method for multiple Kinect devices when compared to the conventional markerbased calibration.
基金This research was funded by the Key Project on Anhui Provincial Natural Science Study by Colleges and Universities under Grant“Key technical research of knee function evaluation and rehabilitation training”(No.KJ2019A0555)Key project of Science and Technology Service Network Program of Chinese Academy of Sciences“Construction of chronic disease risk prevention and control service system based on big data”(No.KFJ-STS-ZDTP-079)Major Science and Technology Projects ofAnhui Province”Research and demonstration of key technologies of non-medical sexual health promotion services”(No.18030801133).
文摘Timely and effective knee function evaluation and knee exercises promote the prevention and self-management of knee diseases.In this paper,aKinectbased exergame system is proposed to assess and train the knee function.Azure Kinect was used to capture and generate 3D models of the user and immerse them in an interactive virtual environment.The software included three functional modules:knee function evaluation,Knee exercises game,and Comprehensive evaluation.The stand,step,leg lift,and squat were selected for knee function evaluation and exercises.Twenty volunteers participated in the experiment.Intraclass correlation coefficients(ICCs)were calculated to assess the reliability of kinematic measurements of knee angles during the movements.The ICC of these movements were stand(ICC=0.987),step(ICC=0.997),left leg lift(ICC=0.981),right leg lift(ICC=0.990),stand(ICC=0.998).The results show that the test-retest reliability is high.It means that the motion capture data is effective and the data obtained by Kinect is stable.
文摘This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedestrian detection algorithms during training. Furthermore, it is dedicated to addressing occlusion issues in pedestrian detection by using Azure Kinect for body tracking and integrating a robust occlusion management algorithm, significantly enhancing detection efficiency. In experiments, an average latency of 204 milliseconds was measured, and the detection accuracy reached an outstanding level of 97%. Additionally, this approach has been successfully applied in creating a simple yet captivating augmented reality game, demonstrating the practical application of the algorithm.
