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The visual analysis of human motion have become a direction of the leading edge of concern in computer vision. The problem of recognizing human actions in video have proven to be a difficult challenge for computer vision. A common trend is to combine shape and motion feature in Bag-of-word (BoW) framework. The BoW framework needs read the whole video for once recognition. It could not be applicable...
In this paper, we present a monocular 3D arm movement tracking system using adaptive particle filter. The effective sample size (ESS) is analyzed in the adaptive particle filter to tackle the abrupt dynamic changes of the arm movement. Sample-efficiency-optimized auxiliary particle filter (SEO-APF) is invoked when low ESS is detected. In SEO-APF, the auxiliary variable weights are computed to minimize...
This paper presents a multi-view articulated human motion tracking framework using particle filter with manifold learning through Gaussian process latent variable model. The dimensionality of the input image observation and joint angles are reduced using Gaussian process models to improve the tracking efficiency. The forward and backward mappings between the two low dimensional spaces are then obtained...
In this paper, a Bayesian mixture expert (BME) framework for the estimation of 3D human poses from two uncalibrated wide-baseline cameras is presented. The two cameras will reduce the ambiguities of the pose estimation greatly and is easy to implement. BME is learnt to conduct multimodal pose estimation regression. K-means algorithm considering Euclidean distance and maximum-value distance for the...
In this paper, a robust 3D dance posture recognition system using two cameras is proposed. A pair of wide-baseline video cameras with approximately orthogonal looking directions is used to reduce pose recognition ambiguities. Silhouettes extracted from these two views are represented using Gaussian mixture models (GMM) and used as features for recognition. Relevance vector machine (RVM) is deployed...
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