Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Curve spreads: a biometric from front-view gait video
Pattern Recognition Letters
Biometric system for person recognition using gait
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Human gait recognition based on signals from two force plates
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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We demonstrate a three-dimensional (for location, time, and magnitude of body part movement) pattern representation of entire time-dependent front-view gait cycle that simultaneously displays the coupled kinetics of different body parts thereby revealing possible irregularities in the gait characteristics of a moving human subject. The time-independent pattern is able to track attendant displacements of other parts (e.g., in the lower body) that result from a movement of a lead part (e.g., in the upper body). It is derived by applying a computationally simple silhouette feature extractor algorithm unto the video footage of the subject that is taken using one stationary CCD camera. The pattern illustrates in a single field-of-view, possible mutual interactions of all four limbs allowing us to identify the types and phases of the gait cycle, observe possible shifting of body weight and other nonlocal effects of the gait pathology. As a data representation, the pattern representation is more compact and easier to store, retrieve, transport and organize. The patterns are easily compared with each other via straightforward image cross-correlation technique. Front-view gait analysis permits an unambiguous and accurate description of the gait dynamics that is not possible with side- or top-view observation. Among the potential applications of our technique are improved diagnosis and treatment of gait pathologies in rehabilitation clinics and modelling schools as well as development of more robust surveillance systems.