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An Empirical Study on Bidirectional Recurrent Neural Networks for Human Motion Recognition
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Tanisaro, Pattreeya; Heidemann, Gunther. - : Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, 2018. : LIPIcs - Leibniz International Proceedings in Informatics. 25th International Symposium on Temporal Representation and Reasoning (TIME 2018), 2018
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Abstract:
The deep recurrent neural networks (RNNs) and their associated gated neurons, such as Long Short-Term Memory (LSTM) have demonstrated a continued and growing success rates with researches in various sequential data processing applications, especially when applied to speech recognition and language modeling. Despite this, amongst current researches, there are limited studies on the deep RNNs architectures and their effects being applied to other application domains. In this paper, we evaluated the different strategies available to construct bidirectional recurrent neural networks (BRNNs) applying Gated Recurrent Units (GRUs), as well as investigating a reservoir computing RNNs, i.e., Echo state networks (ESN) and a few other conventional machine learning techniques for skeleton-based human motion recognition. The evaluation of tasks focuses on the generalization of different approaches by employing arbitrary untrained viewpoints, combined together with previously unseen subjects. Moreover, we extended the test by lowering the subsampling frame rates to examine the robustness of the algorithms being employed against the varying of movement speed.
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Keyword:
Bidirectional Recurrent Neural Networks; Data processing Computer science; Echo State Networks; Human Motion Classification; Motion Capture; Recurrent Neural Networks
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URN:
urn:nbn:de:0030-drops-97865
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URL: https://drops.dagstuhl.de/opus/volltexte/2018/9786/
https://doi.org/10.4230/LIPIcs.TIME.2018.21
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