Open AccessPrior Knowledge Based Motion Model Representation
Author(s) -
Ángel D. Sappa,
Niki Aifanti,
Sotiris Malassiotis,
Michael G. Strintzis
Publication year - 2005
Publication title -
elcvia. electronic letters on computer vision and image analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.15
H-Index - 11
ISSN - 1577-5097
DOI - 10.5565/rev/elcvia.106
Subject(s) - computer science , computer vision , key frame , artificial intelligence , key (lock) , kinematics , process (computing) , motion (physics) , frame (networking) , feature (linguistics) , exploit , representation (politics) , displacement (psychology) , point (geometry) , tracking (education) , trajectory , monocular , mathematics , pedagogy , philosophy , computer security , law , psychotherapist , linguistics , operating system , psychology , telecommunications , geometry , classical mechanics , political science , physics , politics , astronomy
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