Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| DTW-анализ походки× | Безмаркерный захват движения× | |
|---|---|---|
| Область | Биомеханика | Биомеханика |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1978 | 2017 |
| Автор метода≠ | Sakoe and Chiba | Zhe Cao |
| Тип≠ | Sequence alignment and pattern matching | Deep learning pipeline |
| Основополагающий источник≠ | Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. DOI ↗ | 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 ↗ |
| Другие названия | DTW, Gait pattern matching, Temporal gait comparison | Marker-free tracking, Vision-based motion capture, Deep learning pose estimation |
| Связанные | 3 | 3 |
| Сводка≠ | Dynamic Time Warping (DTW) is a sequence alignment algorithm that measures similarity between time series of different lengths by allowing flexible temporal matching. Applied to gait analysis, DTW enables comparison of walking patterns across subjects and conditions despite variations in cadence or stride length. | 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. |
| ScholarGateНабор данных ↗ |
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