| Presentation | 2017-01-19 Fish Detection and Recognition for AR-based Learning Support System in an Aquarium Chihiro Dogo, Tatsuya Kobayashi, Masaru Sugano, Seiichiro Hangai, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper presents fish detection and recognition method for AR-based learning support system, which helps users to learn about fishes swimming in an aquarium. The proposed method analyzes pictures of a water tank taken from the outside of the tank, and detects particular types of fish trained beforehand. The existing detection and recognition method lacks precision for practical use. Therefore, we apply automatic blurred image generation of training images, background subtraction and Kalman filtering to the conventional method. Experimental results show the improvement of detection accuracy. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Learning Support System / Deep Learning / Generic Object Recognition / Augmented Reality |
| Paper # | PRMU2016-139,MVE2016-30 |
| Date of Issue | 2017-01-12 (PRMU, MVE) |
| Conference Information | |
| Committee | PRMU / IPSJ-CVIM / MVE |
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| Conference Date | 2017/1/19(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Eisaku Maeda(NTT) / / Yoshinari Kameda(Univ. of Tsukuba) |
| Vice Chair | Seiichi Uchida(Kyushu Univ.) / Hironobu Fujiyoshi(Chubu Univ.) / / Kenji Mase(Nagoya Univ.) |
| Secretary | Seiichi Uchida(Kyoto Univ.) / Hironobu Fujiyoshi(NTT) / / Kenji Mase(Kyushu Univ.) |
| Assistant | Masaki Oonishi(AIST) / Takuya Funatomi(NAIST) / / Hideaki Uchiyama(Kyushu Univ.) / Takatsugu Hirayama(Nagoya Univ.) / Ryosuke Aoki(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding / Special Interest Group on Computer Vision and Image Media / Technical Committee on Multimedia and Virtual Environment |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Fish Detection and Recognition for AR-based Learning Support System in an Aquarium |
| Sub Title (in English) | |
| Keyword(1) | Learning Support System |
| Keyword(2) | Deep Learning |
| Keyword(3) | Generic Object Recognition |
| Keyword(4) | Augmented Reality |
| 1st Author's Name | Chihiro Dogo |
| 1st Author's Affiliation | Tokyo University of Science(TUS) |
| 2nd Author's Name | Tatsuya Kobayashi |
| 2nd Author's Affiliation | KDDI Research, Inc.(KDDI Research) |
| 3rd Author's Name | Masaru Sugano |
| 3rd Author's Affiliation | KDDI Research, Inc.(KDDI Research) |
| 4th Author's Name | Seiichiro Hangai |
| 4th Author's Affiliation | Tokyo University of Science(TUS) |
| Date | 2017-01-19 |
| Paper # | PRMU2016-139,MVE2016-30 |
| Volume (vol) | vol.116 |
| Number (no) | PRMU-411,MVE-412 |
| Page | pp.pp.165-169(PRMU), pp.165-169(MVE), |
| #Pages | 5 |
| Date of Issue | 2017-01-12 (PRMU, MVE) |