@inproceedings{4191ba9e364d4f27b2806597dad915d6,
title = "Hierarchical dynamic parsing and encoding for action recognition",
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",
author = "Bing Su and Jiahuan Zhou and Xiaoqing Ding and Hao Wang and Ying Wu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 14th European Conference on Computer Vision, ECCV 2016 ; Conference date: 08-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-46493-0_13",
language = "English (US)",
isbn = "9783319464923",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "202--217",
editor = "Bastian Leibe and Jiri Matas and Nicu Sebe and Max Welling",
booktitle = "Computer Vision - 14th European Conference, ECCV 2016, Proceedings",
}