| Presentation | 2016-08-24 Daily Activity Recognition Based on Recurrent Neural Network Akira Tamamori, Tomoki Hayashi, Tomoki Toda, Kazuya Takeda, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Our goal is to build an automatic surveillance system for elderly people and the core technique is daily activity recognition. In previous work, the effectiveness of Deep Neural Network~(DNN) has been shown in the daily activity recognition experiments, by using the database of actual daily activity for 48 hours continuous recordings. In DNN, however, the recognition performance was not enough. We realized that this is because the scope of temporal context to be taken into account is limited, and further improvement of the performance will be needed. In this study, we apply Recurrent Neural Network based on Long Short Term Memory (LSTM-RNN) and Bidirectional LSTM-RNN (BLSTM-RNN) to improve recognition performance. It is expected that LSTM-RNN can capture longer term temporal context. We further investigate the optimal network architecture. The experimental results of daily activity recognition shows the effectiveness of LSTM-RNN and BLSTM-RNN compared to DNN. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Daily Activity Recognition / Deep Neural Network / Recurrent Neural Network / Long Short Term Memory |
| Paper # | SP2016-28 |
| Date of Issue | 2016-08-17 (SP) |
| Conference Information | |
| Committee | SP |
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| Conference Date | 2016/8/24(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | ACCMS, Kyoto Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Audio event processing, etc. |
| Chair | Kazunori Mano(Shibaura Inst. of Tech.) |
| Vice Chair | Hiroki Mori(Utsunomiya Univ.) |
| Secretary | Hiroki Mori(Kobe Univ.) |
| Assistant | Taichi Asami(NTT) / Kei Hashimoto(Nagoya Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Speech |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Daily Activity Recognition Based on Recurrent Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Daily Activity Recognition |
| Keyword(2) | Deep Neural Network |
| Keyword(3) | Recurrent Neural Network |
| Keyword(4) | Long Short Term Memory |
| 1st Author's Name | Akira Tamamori |
| 1st Author's Affiliation | Nagoya University(Nagoya Univ.) |
| 2nd Author's Name | Tomoki Hayashi |
| 2nd Author's Affiliation | Nagoya University(Nagoya Univ.) |
| 3rd Author's Name | Tomoki Toda |
| 3rd Author's Affiliation | Nagoya University(Nagoya Univ.) |
| 4th Author's Name | Kazuya Takeda |
| 4th Author's Affiliation | Nagoya University(Nagoya Univ.) |
| Date | 2016-08-24 |
| Paper # | SP2016-28 |
| Volume (vol) | vol.116 |
| Number (no) | SP-189 |
| Page | pp.pp.7-12(SP), |
| #Pages | 6 |
| Date of Issue | 2016-08-17 (SP) |