This paper presents a new approach for human walking modeling from monocular image sequences. A kinematics model and a walking motion model are introduced in order to exploit prior knowledge. The proposed technique consists of two steps. Initially, an efficient feature point selection and tracking approach is used to compute feature points' trajectories. Peaks and valleys of these trajectories are used to detect key frames-frames where both legs are in contact with the floor. Secondly, motion models associated with each joint are locally tuned by using those key frames. Differently than previous approaches, this tuning process is not performed at every frame, reducing CPU time. In addition, the movement's frequency is defined by the elapsed time between two consecutive key frames, which allows handling walking displacement at different speed. Experimental results with different video sequences are presented.
Prior Knowledge Based Motion Model Representation
01.11.2005
doi:10.5565/rev/elcvia.106
ELCVIA Electronic Letters on Computer Vision and Image Analysis; Vol. 5 No. 3 (2005): Special issue on Articulated Motion & Deformable Objects - August 2005; 55 - 67 ; 1577-5097
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
Representation Learning for Robot Keypoint Detection using Prior Kinematic Knowledge
Deutsches Zentrum für Luft- und Raumfahrt (DLR) | 2022
|Introducing prior knowledge for a hybrid accident prediction model
Springer Verlag | 2016
|Introducing prior knowledge for a hybrid accident prediction model
Online Contents | 2017
|3D reconstruction using prior knowledge
TIBKAT | 2025
|Incorporating Prior Knowledge in ICA
British Library Online Contents | 2004
|