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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44048

Title: Occluded Human Action Analysis Using Dynamic Manifold Model
Authors: Li-Chih Chen
Jun-Wei Hsieh
Chi-Hung Chuang
Chang-Yu Huang
D. Y. Chen
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Humans
Manifolds
Context,
Analytical models
Accuracy
Matrix converters
Educational institutions
Date: 2012-11
Issue Date: 2017-11-13T07:12:05Z
Publisher: 2012 IEEE International Conference on Pattern Recognition
Abstract: Abstract:This paper proposes a novel nonlinear manifold learning method for addressing the ill-posed problem of occluded human action analysis. As we know, a person can perform a broad variety of movements. To capture the multiplicity of a human action, this paper creates a low-dimensional manifold for capturing the intra-path and inter-path contexts of an event. Then, an action path matching scheme can be applied for seeking the best event path for linking the missed information between occluded persons. After that, a recovering scheme is proposed for repairing an occluded object to a complete one. Then, each action can be converted to a series of action primitives through posture analysis. Since occluded objects are handled, there will be many posture-symbol-converting errors. Instead of using a specific symbol, we code a posture using not only its best matched key posture but also its similarities among other key postures. Then, recognition of an action taken from occlude objects can be modeled as a matrix matching problem. With the matrix representation, different actions can be more robustly and effectively matched by comparing their Kullback-Leibler(KL) distances.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44048
Appears in Collections:[資訊工程學系] 演講及研討會

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