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Hierarchical relaxed partitioning system for activity recognition

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Azhar, Faisal and Li, Chang-Tsun (2017) Hierarchical relaxed partitioning system for activity recognition. IEEE Transactions on Cybernetics, 47 (3). pp. 784-795. doi:10.1109/TCYB.2016.2526970

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Official URL: http://doi.org/10.1109/TCYB.2016.2526970

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Abstract

A hierarchical relaxed partitioning system (HRPS) is proposed for recognizing similar activities which has a feature space with multiple overlaps. Two feature descriptors are built from the human motion analysis of a 2D stick figure to represent cyclic and non-cyclic activities. The HRPS first discerns the pure and impure activities, i.e., with no overlaps and multiple overlaps in the feature space respectively, then tackles the multiple overlaps problem of the impure activities via an innovative majority voting scheme. The results show that the proposed method robustly recognises various activities of two different resolution data sets, i.e., low and high (with different views). The advantage of HRPS lies in the real-time speed, ease of implementation and extension, and non-intensive training.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Decision trees, Human activity recognition
Journal or Publication Title: IEEE Transactions on Cybernetics
Publisher: IEEE Computer Society
ISSN: 2168-2267
Official Date: March 2017
Dates:
DateEvent
March 2017Published
12 July 2016Accepted
2015Submitted
Volume: 47
Number: 3
Number of Pages: 12
Page Range: pp. 784-795
DOI: 10.1109/TCYB.2016.2526970
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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