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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44092

Title: Modeling and Recognizing Action Contexts in Persons Using Sparse Representation
Authors: Kai-Ting Chuang
Jun-Wei Hsieh
Yilin Yan
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Sparse Coding
Sparse Representation
daily events
Date: 2015-07
Issue Date: 2017-11-14T08:21:52Z
Publisher: Journal of Visual Communication and Image Representation
Abstract: Abstract:This paper proposes a novel dynamic sparse representation-based classification scheme to treat the problem of interaction action analysis between persons using sparse representation. The occlusion problem and the difficulty to model complicated interactions are the major challenges in person-to-person action analysis. To address the occlusion problem, the proposed scheme represents an action sample in an over-complete dictionary whose base elements are the training samples themselves. This representation is naturally sparse and makes errors (caused by different environmental changes like lighting or occlusions) sparsely appear in the training library. Because of the sparsity, it is robust to occlusions and lighting changes. The difficulty of complicated action modeling can be tackled by adding more examples to the over-complete dictionary. Thus, even though the interaction relations are complicated, the proposed method still works successfully to recognize them and can be easily extended to analyze action events among multiple persons.
Relation: 30, pp.252–265
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44092
Appears in Collections:[資訊工程學系] 期刊論文

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