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Intelligent Service Robotics
Erschienen in: Intelligent Service Robotics 1/2017

14.10.2016 | Original Research Paper

Motion codeword generation using selective subsequence clustering for human action recognition

verfasst von: Woo Young Kwon, Sang Hyoung Lee, Il Hong Suh

Erschienen in: Intelligent Service Robotics | Ausgabe 1/2017

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Abstract

The understanding of human activity is one of the key research areas in human-centered robotic applications. In this paper, we propose complexity-based motion features for recognizing human actions. Using a time-series-complexity measure, the proposed method evaluates the amount of useful information in subsequences to select meaningful temporal parts in a human motion trajectory. Based on these meaningful subsequences, motion codewords are learned using a clustering algorithm. Motion features are then generated and represented as a histogram of the motion codewords. Furthermore, we propose a multiscaled sliding window for generating motion codewords to solve the sensitivity problem of the performance to the fixed length of the sliding window. As a classification method, we employed a random forest classifier. Moreover, to validate the proposed method, we present experimental results of the proposed approach based on two open data sets: MSR Action 3D and UTKinect data sets.
Vorheriger Artikel Control of bidirectional physical human–robot interaction based on the human intention
Nächster Artikel Robust mapping and localization in indoor environments

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Metadaten
Titel
Motion codeword generation using selective subsequence clustering for human action recognition
verfasst von
Woo Young Kwon
Sang Hyoung Lee
Il Hong Suh
Publikationsdatum
14.10.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Intelligent Service Robotics / Ausgabe 1/2017
Print ISSN: 1861-2776
Elektronische ISSN: 1861-2784
DOI
https://doi.org/10.1007/s11370-016-0208-3

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