Hierarchical dynamic parsing and encoding for action recognition

Bing Su; Jiahuan Zhou; Xiaoqing Ding; Hao Wang; Ying Wu

doi:10.1007/978-3-319-46493-0_13

Hierarchical dynamic parsing and encoding for action recognition

Bing Su^*, Jiahuan Zhou, Xiaoqing Ding, Hao Wang, Ying Wu

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

17 Scopus citations

Abstract

A video action generally exhibits quite complex rhythms and non-stationary dynamics. To model such non-uniform dynamics, this paper describes a novel hierarchical dynamic encoding method to capture both the locally smooth dynamics and globally drastic dynamic changes. It provides a multi-layer joint representation for temporal modeling for action recognition. At the first layer, the action sequence is parsed in an unsupervised manner into several smooth-changing stages corresponding to different key poses or temporal structures. The dynamics within each stage are encoded by mean-pooling or learning to rank based encoding. At the second layer, the temporal information of the ordered dynamics extracted from the previous layer is encoded again to form the overall representation. Extensive experiments on a gesture action dataset (Chalearn) and several generic action datasets (Olympic Sports and Hol-lywood2) have demonstrated the effectiveness of the proposed method.

Original language	English (US)
Title of host publication	Computer Vision - 14th European Conference, ECCV 2016, Proceedings
Editors	Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Publisher	Springer Verlag
Pages	202-217
Number of pages	16
ISBN (Print)	9783319464923
DOIs	https://doi.org/10.1007/978-3-319-46493-0_13
State	Published - 2016
Event	14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands Duration: Oct 8 2016 → Oct 16 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9908 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th European Conference on Computer Vision, ECCV 2016
Country/Territory	Netherlands
City	Amsterdam
Period	10/8/16 → 10/16/16

Funding

This work was supported by National Basic Research Program of China (2013CB329305, 2013CB329403), Natural Science Foundation of China (61303164, 61402447, 61471214, 61502466) and Development Plan of Outstanding Young Talent from Institute of Software, Chinese Academy of Sciences (ISCAS2014-JQ02). This work was also supported by National Science Foundation grant IIS-0916607, IIS-1217302.

Keywords

Action recognition
Dynamic encoding
Hierarchical modeling

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46493-0_13

Cite this

Su, B., Zhou, J., Ding, X., Wang, H., & Wu, Y. (2016). Hierarchical dynamic parsing and encoding for action recognition. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision - 14th European Conference, ECCV 2016, Proceedings (pp. 202-217). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9908 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46493-0_13

Su, Bing ; Zhou, Jiahuan ; Ding, Xiaoqing et al. / Hierarchical dynamic parsing and encoding for action recognition. Computer Vision - 14th European Conference, ECCV 2016, Proceedings. editor / Bastian Leibe ; Jiri Matas ; Nicu Sebe ; Max Welling. Springer Verlag, 2016. pp. 202-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A video action generally exhibits quite complex rhythms and non-stationary dynamics. To model such non-uniform dynamics, this paper describes a novel hierarchical dynamic encoding method to capture both the locally smooth dynamics and globally drastic dynamic changes. It provides a multi-layer joint representation for temporal modeling for action recognition. At the first layer, the action sequence is parsed in an unsupervised manner into several smooth-changing stages corresponding to different key poses or temporal structures. The dynamics within each stage are encoded by mean-pooling or learning to rank based encoding. At the second layer, the temporal information of the ordered dynamics extracted from the previous layer is encoded again to form the overall representation. Extensive experiments on a gesture action dataset (Chalearn) and several generic action datasets (Olympic Sports and Hol-lywood2) have demonstrated the effectiveness of the proposed method.",

keywords = "Action recognition, Dynamic encoding, Hierarchical modeling",

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Su, B, Zhou, J, Ding, X, Wang, H & Wu, Y 2016, Hierarchical dynamic parsing and encoding for action recognition. in B Leibe, J Matas, N Sebe & M Welling (eds), Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9908 LNCS, Springer Verlag, pp. 202-217, 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, Netherlands, 10/8/16. https://doi.org/10.1007/978-3-319-46493-0_13

Hierarchical dynamic parsing and encoding for action recognition. / Su, Bing; Zhou, Jiahuan; Ding, Xiaoqing et al.
Computer Vision - 14th European Conference, ECCV 2016, Proceedings. ed. / Bastian Leibe; Jiri Matas; Nicu Sebe; Max Welling. Springer Verlag, 2016. p. 202-217 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9908 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Su, Bing

AU - Zhou, Jiahuan

AU - Ding, Xiaoqing

AU - Wang, Hao

AU - Wu, Ying

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - A video action generally exhibits quite complex rhythms and non-stationary dynamics. To model such non-uniform dynamics, this paper describes a novel hierarchical dynamic encoding method to capture both the locally smooth dynamics and globally drastic dynamic changes. It provides a multi-layer joint representation for temporal modeling for action recognition. At the first layer, the action sequence is parsed in an unsupervised manner into several smooth-changing stages corresponding to different key poses or temporal structures. The dynamics within each stage are encoded by mean-pooling or learning to rank based encoding. At the second layer, the temporal information of the ordered dynamics extracted from the previous layer is encoded again to form the overall representation. Extensive experiments on a gesture action dataset (Chalearn) and several generic action datasets (Olympic Sports and Hol-lywood2) have demonstrated the effectiveness of the proposed method.

AB - A video action generally exhibits quite complex rhythms and non-stationary dynamics. To model such non-uniform dynamics, this paper describes a novel hierarchical dynamic encoding method to capture both the locally smooth dynamics and globally drastic dynamic changes. It provides a multi-layer joint representation for temporal modeling for action recognition. At the first layer, the action sequence is parsed in an unsupervised manner into several smooth-changing stages corresponding to different key poses or temporal structures. The dynamics within each stage are encoded by mean-pooling or learning to rank based encoding. At the second layer, the temporal information of the ordered dynamics extracted from the previous layer is encoded again to form the overall representation. Extensive experiments on a gesture action dataset (Chalearn) and several generic action datasets (Olympic Sports and Hol-lywood2) have demonstrated the effectiveness of the proposed method.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Su B, Zhou J, Ding X, Wang H, Wu Y. Hierarchical dynamic parsing and encoding for action recognition. In Leibe B, Matas J, Sebe N, Welling M, editors, Computer Vision - 14th European Conference, ECCV 2016, Proceedings. Springer Verlag. 2016. p. 202-217. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46493-0_13

Hierarchical dynamic parsing and encoding for action recognition

Abstract

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