Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

Bo Sun; Siming Cao; Jun He; Lejun Yu; Liandong Li

doi:10.1117/1.JEI.26.2.020501

2 March 2017 Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

Bo Sun, Siming Cao, Jun He, Lejun Yu, Liandong Li

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Journal of Electronic Imaging, Vol. 26, Issue 2, 020501 (March 2017). https://doi.org/10.1117/1.JEI.26.2.020501

Abstract

Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal–spatial features, which synthesizes the local and global temporal–spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Citation Download Citation

Bo Sun, Siming Cao, Jun He, Lejun Yu, and Liandong Li "Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks," Journal of Electronic Imaging 26(2), 020501 (2 March 2017). https://doi.org/10.1117/1.JEI.26.2.020501

Received: 1 December 2016; Accepted: 14 February 2017; Published: 2 March 2017

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