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This paper proposes an approach for segmenting single actions from continuously captured motion sequences by examining the properties of active limbs. The target motions are related to sporting and dancing. In particular, two types of human sports motions are examined: 1) boxing and 2) hip hop dance. To segment continuous boxing motion sequences into single punches and combo punches, this paper employs...
The purpose of this research is to analysis feature parameters of Suriashi (sliding gait motion in Japanese traditional performing arts and Japanese traditional martial arts) with multivariate data analysis and to discriminate Suriashi movement among other gait motions using SVM (Support Vector Machine). Experiments were carried out using motion capture on the Suriashi of Japanese Traditional Dance...
This study investigates recognition of affect in human walking as daily motion, in order to provide a means for affect recognition at distance. For this purpose, a data base of affective gait patterns from non-professional actors has been recorded with optical motion tracking. Principal component analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) are applied to kinematic parameters...
Moving cast shadows are a major concern for foreground detection algorithms. In this paper, a novel method is proposed to detect the moving cast shadows in the scene. This approach uses GMM to generate the background image, and extract the feature using PCA-based transformation to the background image, then, the feature space is utilized to classify moving shadows and foreground objects. Experimental...
In this paper we propose a gait recognition method with dynamic gait energy image (DGEI) and manifold learning. First we present a new gait feature-dynamic gait energy image which can reflect the dynamic variance parts of the motion body and can better characterize gait features. Secondly in order to preserve the principal and discriminant components, we use PCA and LPP to discover the low-dimensional...
An approach is proposed for abnormal sections detection in video sequences. In this approach, firstly the histogram is selected to describe the color change in the section, and then the histograms of the frames selected from the section compose the histogram matrix. In order to improve the process efficiency, the principal components analysis (PCA) is used to reduce dimensions of the histogram matrix...
A novel strategy for the visual tracking and the information extracted problem is proposed which is for the case of maneuvering target. The strategy contains two methods. One is used to tracking, and the other one is used to extract the information of the vehicle. A non-linear estimation method using the particle filter to track objects is presented. During the tracking, a great deal of vehicle information...
Locally linear embedding (LLE) is a prevalent manifold learning method in pattern recognition and machine learning. It preserves the intrinsic structure information of data set and has been widely applied to feature extraction and dimensionality reduction. This paper introduces LLE to aircraft pose recognition. The representative motion poses of an aircraft in the air are analyzed. Unfolding results...
A new gait method using the periodic sequence width images and kernel based Fisher discriminant analysis is proposed. The gait pattern is described by the periodic sequence width images. It exacts from the width temporal image generated by calculating the width vector sequences and representing the width value in grey level. The periodic sequence width images capture both the shape structure information...
This paper proposes a boosting EigenActions algorithm for human action categorization. In determining the EigenActions, a spatio-temporal information saliency is first calculated from the video sequence by estimating pixel density function. Since human action can be approximated as a periodic motion, salient action unit, which is one cycle of the motion, is extracted and EigenActions are determined...
This paper proposes new features for motion recognition. Higher order local autocorrelation (HLAC) features are extracted from the motion history images (MHI). Since MHI calculated from the video images include important motion information, it is expected that HLAC features extracted from MHI have good properties for motion recognition. The proposed features were tested using image sequences of pitching...
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
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