| Presentation | 2022-02-22 Classification of User's Device Possession Position and Behavior by Using Deep Metric Learning Rui Kitahara, Lifeng Zhang, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | With the widespread use of smartphones, there have been efforts to classify human behavior using built-in sensors. However, most of these efforts are limited to a single location where the smartphone is held and are considered insufficient to be incorporated into actual smartphones as a system. If it can be confirmed that it is possible to classify behavior and possession location at the same time, it will be possible to change the notification method of the smartphone according to the user's situation. In this study, we acquired data from the accelerometer of a smartphone, trained it using deep metric learning, and classified the user's behavior and possession position using cosine similarity during inference. As a result, not only did we obtain the same accuracy as in the previous study even when classifying both actions and possession positions at the same time, but we also confirmed that it was possible to output untrained data as an unknown class. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Behavioral Classification / Accelerometer / Image Processing / Deep Learning |
| Paper # | ITS2021-40,IE2021-49 |
| Date of Issue | 2022-02-14 (ITS, IE) |
| Conference Information | |
| Committee | IE / ITS / ITE-AIT / ITE-ME / ITE-MMS |
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| Conference Date | 2022/2/21(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Image Processing, etc. |
| Chair | Kazuya Kodama(NII) / Masahiro Fujii(Utsunomiya Univ.) / Hisaki Nate(Tokyo Polytechnic Univ.) / Hiroyuki Arai(Nippon Inst. of Tech.) / Kenji Machida(NHK) |
| Vice Chair | Hiroyuki Bandoh(NTT) / Toshihiko Yamazaki(Univ. of Tokyo) / Kohei Ohno(Meiji Univ.) / Naohisa Hashimoto(AIST) / / Shogo Muramatsu(Niigata Univ.) |
| Secretary | Hiroyuki Bandoh(KDDI Research) / Toshihiko Yamazaki(Nagoya Inst. of Tech.) / Kohei Ohno(Akita Prefectural Univ.) / Naohisa Hashimoto(NIT, Tsuruoka College) / / Shogo Muramatsu(NHK) / (Hokkaido Univ.) |
| Assistant | Shunsuke Iwamura(NHK) / Shinobu Kudo(NTT) / Msataka Imao(Mitsubishi Electric) / Kenshi Saho(Toyama Prefectural Univ.) / Keiji Jimi(Gunma Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Image Engineering / Technical Committee on Intelligent Transport Systems Technology / Technical Group on Artistic Image Technology / Technical Group on Media Engineering / Technical Group on Multi-media Storage |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Classification of User's Device Possession Position and Behavior by Using Deep Metric Learning |
| Sub Title (in English) | |
| Keyword(1) | Behavioral Classification |
| Keyword(2) | Accelerometer |
| Keyword(3) | Image Processing |
| Keyword(4) | Deep Learning |
| 1st Author's Name | Rui Kitahara |
| 1st Author's Affiliation | Kyushu Institute of Technology(Kyutech) |
| 2nd Author's Name | Lifeng Zhang |
| 2nd Author's Affiliation | Kyushu Institute of Technology(Kyutech) |
| Date | 2022-02-22 |
| Paper # | ITS2021-40,IE2021-49 |
| Volume (vol) | vol.121 |
| Number (no) | ITS-373,IE-374 |
| Page | pp.pp.91-96(ITS), pp.91-96(IE), |
| #Pages | 6 |
| Date of Issue | 2022-02-14 (ITS, IE) |