Supervised spatio-temporal kernel descriptor for human action recognition from RGB-depth videos

Supervised spatio-temporal kernel descriptor for human action recognition from RGB-depth videos One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class variations and maximizing the inter-class distances. Subsequently, the efficient match kernel (EMK) is used to abstract the mid-level kernel features for a more efficient classification. The proposed approach is evaluated on five public benchmark datasets. The experimental evaluations demonstrate that the proposed method achieves superior performance to the state-of-the-art methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Supervised spatio-temporal kernel descriptor for human action recognition from RGB-depth videos

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-5017-y
Publisher site
See Article on Publisher Site

Abstract

One of the most challenging tasks in computer vision is human action recognition. The recent development of depth sensors has created new opportunities in this field of research. In this paper, a novel supervised spatio-temporal kernel descriptor (SSTKDes) is proposed from RGB-depth videos to establish a discriminative and compact feature representation of actions. To enhance the descriptive and discriminative ability of the descriptor, extracted primary kernel-based features are transformed into a new space by exploiting a supervised training strategy; i.e., large margin nearest neighbor (LMNN). The LMNN highly reduces the error of a nearest neighbor classifier by minimizing the intra-class variations and maximizing the inter-class distances. Subsequently, the efficient match kernel (EMK) is used to abstract the mid-level kernel features for a more efficient classification. The proposed approach is evaluated on five public benchmark datasets. The experimental evaluations demonstrate that the proposed method achieves superior performance to the state-of-the-art methods.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jul 21, 2017

References

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