Σύγκριση μεθόδων

	Σύλληψη κίνησης χωρίς δείκτες	Ανάλυση Βάδισης με Δυναμική Παραμόρφωση Χρόνου (DTW)
Πεδίο	Εμβιομηχανική	Εμβιομηχανική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2017	1978
Δημιουργός	Zhe Cao	Sakoe and Chiba
Τύπος	Deep learning pipeline	Sequence alignment and pattern matching
Θεμελιώδης πηγή	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).	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.
Εναλλακτικές ονομασίες	Marker-free tracking, Vision-based motion capture, Deep learning pose estimation	DTW, Gait pattern matching, Temporal gait comparison
Συναφείς	3	3
Σύνοψη	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.	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.