Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Безмаркерный захват движения× | Прямая кинематика× | |
|---|---|---|
| Область | Биомеханика | Биомеханика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2017 | 1986 |
| Автор метода≠ | Zhe Cao | John Craig |
| Тип≠ | Deep learning pipeline | Computational geometric pipeline |
| Основополагающий источник≠ | Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). DOI ↗ | Craig, J. J. (2005). Introduction to Robotics: Mechanics and Control (3rd ed.). Pearson. link ↗ |
| Другие названия | Marker-free tracking, Vision-based motion capture, Deep learning pose estimation | FK, Kinematic chain, Anatomical chain |
| Связанные | 3 | 3 |
| Сводка≠ | Markerless motion capture infers the 3D positions and joint angles of a moving subject from video sequences using computer vision and machine learning. Pioneered by deep learning approaches such as OpenPose and MediaPipe, it eliminates the need for reflective markers or inertial sensors, making motion capture accessible and practical for real-world applications. | Forward kinematics is the calculation of the position and orientation of a distal body segment (such as the hand) based on the joint angles of proximal segments. Originally formalized in robotics by John Craig and adapted to biomechanics, it allows practitioners to predict endpoint location from known joint configuration. |
| ScholarGateНабор данных ↗ |
|
|