Stochastic kinematic modeling and feature extraction for gait analysis

Dockstader, S.L.; Berg, M.; Tekalp, A.M.

doi:10.1109/tip.2003.815259

Cited by 59 publications

(25 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Urtasun and Fua 26 proposed three-dimensional (3-D) temporal models to track and recover motion parameters. Dockstader et al 27 proposed a hierarchical model, which used a set of thick lines joined at a single point to represent the legs and a periodic pendulum motion model to describe the gait pattern. In Wang et al's work, 28 the human body was modeled as 14 rigid parts connected to one another at the joints with a total of 48 degrees of freedom (DOFs).…”

Section: Gait Recognition Reviewmentioning

confidence: 99%

Genetic feature selection for gait recognition

Tafazzoli

Bebis

Louis

et al. 2015

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. Many research studies have demonstrated that gait can serve as a useful biometric modality for human identification at a distance. Traditional gait recognition systems, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised performance. We investigate the problem of selecting a subset of the most relevant gait features for improving gait recognition performance. This is achieved by discarding redundant and irrelevant gait features while preserving the most informative ones. Motivated by our previous work on feature subset selection using genetic algorithms (GAs), we propose using GAs to select an optimal subset of gait features. First, features are extracted using kernel principal component analysis (KPCA) on spatiotemporal projections of gait silhouettes. Then, GA is applied to select a subset of eigenvectors in KPCA space that best represents a subject's identity. Each gait pattern is then represented by projecting it only on the eigenvectors selected by the GA. To evaluate the effectiveness of the selected features, we have experimented with two different classifiers: k nearest-neighbor and Naïve Bayes classifier. We report considerable gait recognition performance improvements on the Georgia Tech and CASIA databases.

show abstract

Section: Gait Recognition Reviewmentioning

confidence: 99%

Genetic feature selection for gait recognition

Tafazzoli

Bebis

Louis

et al. 2015

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…Model-based methods (e.g., [8,9,10,11]) characterise a human subject using a structural model to measure time-varying gait parameters, e.g., gait period, stance width and stride length, and a motion model to analyse the kinematic and dynamical motion parameters of the subject, e.g., rotation patterns of hip and thigh, joint angle trajectories and orientation change of limbs.…”

Section: Introductionmentioning

confidence: 99%

Robust view-invariant multiscale gait recognition

Das

Tjahjadi

2015

Pattern Recognition

View full text Add to dashboard Cite

The paper proposes a two-phase view-invariant multiscale gait recognition method (VI-MGR) which is robust to variation in clothing and presence of a carried item. In phase 1, VI-MGR uses the entropy of the limb region of a gait energy image (GEI) to determine the matching gallery view of the probe using 2-dimensional principal component analysis and Euclidean distance classifier. In phase 2, the probe subject is compared with the matching view of the gallery subjects using multiscale shape analysis. In this phase, VI-MGR applies Gaussian filter to a GEI to generate a multiscale gait image for gradually highlighting the subject's inner shape characteristics to achieve insensitiveness to boundary shape alterations due to carrying conditions and clothing variation. A weighted random subspace learning based classification is used to exploit the high dimensionality of the feature space for improved identification by avoiding overlearning. Experimental analyses on public datasets demonstrate the efficacy of VI-MGR.

show abstract

“…Model-based methods (e.g., [7,8,9,10,11,12]) characterise a subject by a structural model and a motion model to mainly analyse dynamics of gait [2]. The structural model represents the subject by a stick figure, ellipsoidal fits or a volumetric model based on the proportions of the human body parts, and measures time-varying gait parameters, e.g., gait period, stance width and stride length for gait signatures.…”

Section: Introductionmentioning

confidence: 99%

Gait recognition based on shape and motion analysis of silhouette contours

Das

Tjahjadi

2013

Computer Vision and Image Understanding

View full text Add to dashboard Cite

This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject's silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject's shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject's back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject's leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods.

show abstract

Stochastic kinematic modeling and feature extraction for gait analysis

Cited by 59 publications

References 37 publications

Genetic feature selection for gait recognition

Genetic feature selection for gait recognition

Robust view-invariant multiscale gait recognition

Gait recognition based on shape and motion analysis of silhouette contours

Contact Info

Product

Resources

About