Gait Classification with HMMs for Trajectories of Body Parts Extracted by Mixture Densities
نویسندگان
چکیده
In this paper we describe a system for automatic gait analysis. Different kinds of human gait are recognized using sequences of grey–level images. No markers are needed to get the trajectories of different body parts. The tracking of body parts and the classification are based on statistical models. We model several body parts and the background as mixture densities. The positions are determined iteratively, we begin with the most stable part to find. The anatomy of a human body restricts the area to search for the next one. From the trajectories, features for gait analysis are derived. These are used to train hidden Markov models (HMMs), one HMM for each kind of gait.
منابع مشابه
Model Based Extraction of Articulated Objects in Image Sequencesfor Gait
This paper describes an approach to the extraction of articulated objects which will be used for gait analysis. In most medical applications markers are used to determine trajectories of diierent body parts. This approach works without any markers. Monotony operators which compute the displacement vector eld are used to initialize a contour based tracking algorithm | called active rays | for se...
متن کاملOptimal Trajectory Generation for a Robotic Worm via Parameterization by B-Spline Curves
In this paper we intend to generate some set of optimal trajectories according to the number of control points has been applied for parameterizing those using B-spline curves. The trajectories are used to generate an optimal locomotion gait in a crawling worm-like robot. Due to gait design considerations it is desired to minimize the required torques in a cycle of gait. Similar to caterpillars,...
متن کاملHuman Gait Classi cation Based on Hidden
This paper describes a system for automatic gait analysis. In most clinical systems markers are used to determine the trajectories. We use a system for object recognition without segmentation to track body parts. From these trajectories periodic features are extracted. Another method to determine feature vectors is based on the optical ow computed by monotony operators. Both methods do not pres...
متن کاملHuman Identification Based on Extracted Gait Features
This paper presents a human identification system based on automatically extracted gait features. The proposed approach consists of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by three classification techniques: fuzzy knearest neighbour, linear discriminate analysis and linear support vector machi...
متن کاملCritical features for the perception of emotion from gait.
Human observers readily recognize emotions expressed in body movement. Their perceptual judgments are based on simple movement features, such as overall speed, but also on more intricate posture and dynamic cues. The systematic analysis of such features is complicated due to the difficulty of considering the large number of potentially relevant kinematic and dynamic parameters. To identify emot...
متن کامل