Exploring motion capture algorithms in computer vision using intel depth camera

M.D.Ollaberganova

T.A. Xudaybergenov

##semicolon## motion capture##common.commaListSeparator## virtual reality


सार

The analysis of existing approaches to tracking the human body revealed the presence of problems when capturing movements in a three-dimensional coordinate system. The promise of motion capture systems based on computer vision is noted. Existing research on markerless motion capture systems only considers positioning in 2D space. Therefore, the goal of the study was to improve the accuracy of determining the coordinates of the human body in three-dimensional coordinates by developing a motion capture method based on computer vision and triangulation algorithms.


##submission.citations##

1. Lind C.M., Abtahi F., Forsman M. Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics – An Overview of Current Applications, Challenges, and Future Opportunities. Sensors. 2023;23(9):4259.

2. Sers R., Forrester S., Moss E., Ward S., Ma J., Zecca M. Validity of the Perception Neuron Inertial Motion Capture System for Upper Body Motion Analysis. Measurement. 2020;149:107024.

3. Bauer P., Lienhart W., Jost S. Accuracy Investigation of the Pose Determination of a VR System. Sensors. 2021;21(5):1622.

4. Irshad M.T., Nisar M.A., Gouverneur P., Rapp M., Grzegorzek M. AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review. Sensors. 2020;20(18):5321.

5. Merriaux P., Dupuis Y., Boutteau R., Vasseur P., Savatier X. A Study of Vicon System Positioning Performance. Sensors. 2017;17(7):1591.

6. Nakano N., Sakura T., Ueda K., Omura L., Kimura A., Iino Y., et al. Evaluation of 3D Markerless Motion Capture Accuracy Using OpenPose with Multiple Video Cameras. Frontiers in Sports and Active Living. 2020;2:50.

7. Coronado E., Fukuda K., Ramirez-Alpizar I.G., Yamanobe N., Venture G., Harada K.

Assembly Action Understanding from Fine-Grained Hand Motions, a Multi-camera and Deep Learning Approach. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). New York, NY: IEEE; 2021. P. 26282634.

8. Tausif Diwan, Anirudh G., Tembhurne J.V. Object Detection Using YOLO: Challenges, Architectural Successors, Datasets and Applications. Multimedia Tools and Applications. 2023;82(6):9243–9275.

9. Wei Liu, Anguelov D., Erhan D., Szegedy C., Reed S., Cheng-Yang Fu., et al. SSD: Single Shot MultiBox Detector. In book: Leibe B., Matas J., Sebe N., Welling M. (eds). Computer Vision – ECCV 2016. Cham: Springer. 2016;9905:21– 37.

10. Bharati P., Pramanik A. Deep Learning Techniques—R-CNN to Mask R-CNN: A Survey. In book: Das A., Nayak J., Naik B., Pati S., Pelusi D. (eds). Computational Intelligence in Pattern Recognition. New York, NY: Springer. 2020;999:657–668.

11. Bajpai R., Joshi D. MoveNet: A Deep Neural Network for Joint Profile Prediction across Variable Walking Speeds and Slopes. IEEE Transactions on Instrumentation and Measurement. 2021;70:1–11.

12. Ghanbari S., Ashtyani Z.P., Masouleh M.T. User Identification Based on Hand Geometrical Biometrics Using Media-Pipe. In: Proc. 30th International Conference on Electrical Engineering (ICEE). New York, NY: IEEE; 2022. P. 373–378.

13. Weijian Mai, Fengjie Wu, Ziqian Guo, Yuhan Xiang, Gensheng Liu, Xiaobin Chen. A Fall Detection Alert System Based on Lightweight Openpose and Spatial-Temporal Graph Convolution Network. Journal of Physics: Conference Series. 2021;2035:012036.

14. Szeliski R. Recognition. In book: Computer Vision: Algorithms and Applications.London: Springer; 2011. P. 575–640