Occluded human action analysis using dynamic manifold model

Li Chih Chen*, Jun-Wei Hsieh, Chi Hung Chuang, Chang Yu Huang, D. Y. Chen

*Corresponding author for this work

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題ICPR 2012 - 21st International Conference on Pattern Recognition
頁面1245-1248
頁數4
出版狀態Published - 1 十二月 2012
事件21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
持續時間: 11 十一月 201215 十一月 2012

出版系列

名字Proceedings - International Conference on Pattern Recognition
ISSN(列印)1051-4651

Conference

Conference21st International Conference on Pattern Recognition, ICPR 2012
國家Japan
城市Tsukuba
期間11/11/1215/11/12

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