Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition

Sungwoo Jun; Yoonsik Choe

doi:10.1109/IPAS.2018.8708878

Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition

Sungwoo Jun, Yoonsik Choe

Department of Electrical and Electronic Engineering

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

2 Citations (Scopus)

Abstract

Recently, Recurrent Neural Network (RNN) based approach presents good performance in skeleton based action recognition. Utilizing deep layers of RNN or Long-Short Term Memory (LSTM) has its on weakness when handling long-sequence data because of vanishing or exploding gradients through time. Batch Normalized LSTM (BN-LSTM) is able to give a solution for the problem, with the merit of converging faster in training. In contrast, when deeply layered, BN-LSTM structure shows slow convergence and worse accuracy. In this work, we analyze deep-layered BN-LSTM shows slower convergence in early training phase in training scheme and, finally we propose a deep BN-LSTM structure with auxiliary classifier that is able to converge faster and gives better results at skeleton based action recognition problem. Some experiment are conducted with Penn Action dataset and our own Computer Assembling Video dataset, we verified our proposal shows better results in skeleton based action recognition.

Original language	English
Title of host publication	IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	279-284
Number of pages	6
ISBN (Electronic)	9781728102474
DOIs	https://doi.org/10.1109/IPAS.2018.8708878
Publication status	Published - 2018 Jul 2
Event	3rd IEEE International Conference on Image Processing, Applications and Systems, IPAS 2018 - Sophia Antipolis, France Duration: 2018 Dec 12 → 2018 Dec 14

Publication series

Name	IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018

Conference

Conference	3rd IEEE International Conference on Image Processing, Applications and Systems, IPAS 2018
Country/Territory	France
City	Sophia Antipolis
Period	18/12/12 → 18/12/14

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Hardware and Architecture
Computer Graphics and Computer-Aided Design

Access to Document

10.1109/IPAS.2018.8708878

Cite this

Jun, S., & Choe, Y. (2018). Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition. In IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018 (pp. 279-284). Article 8708878 (IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPAS.2018.8708878

Jun, Sungwoo ; Choe, Yoonsik. / Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition. IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 279-284 (IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018).

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abstract = "Recently, Recurrent Neural Network (RNN) based approach presents good performance in skeleton based action recognition. Utilizing deep layers of RNN or Long-Short Term Memory (LSTM) has its on weakness when handling long-sequence data because of vanishing or exploding gradients through time. Batch Normalized LSTM (BN-LSTM) is able to give a solution for the problem, with the merit of converging faster in training. In contrast, when deeply layered, BN-LSTM structure shows slow convergence and worse accuracy. In this work, we analyze deep-layered BN-LSTM shows slower convergence in early training phase in training scheme and, finally we propose a deep BN-LSTM structure with auxiliary classifier that is able to converge faster and gives better results at skeleton based action recognition problem. Some experiment are conducted with Penn Action dataset and our own Computer Assembling Video dataset, we verified our proposal shows better results in skeleton based action recognition.",

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Jun, S & Choe, Y 2018, Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition. in IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018., 8708878, IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 279-284, 3rd IEEE International Conference on Image Processing, Applications and Systems, IPAS 2018, Sophia Antipolis, France, 18/12/12. https://doi.org/10.1109/IPAS.2018.8708878

Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition. / Jun, Sungwoo; Choe, Yoonsik.
IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 279-284 8708878 (IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018).

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

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Jun S, Choe Y. Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition. In IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 279-284. 8708878. (IEEE 3rd International Conference on Image Processing, Applications and Systems, IPAS 2018). doi: 10.1109/IPAS.2018.8708878

Deep Batch-Normalized LSTM networks with Auxiliary classifier for Skeleton based Action Recognition

Abstract

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