Rits Raccoon Database
www.ritsumei.ac.jp/se/re/izumilab/dist/raccoon.htm
(C) by Reconfigurable Sysems Lab @ Ritsumeikan U, Jun 2017
Movies of Raccoons, Raccoon Dogs, Badger, Fox, and Masked Palm Civet
| Place | Yayoi Ikoi-no Hiroba |
|---|
| Date | Nov 14, 2016 |
|---|
| Format | png, mp4(H.265) |
|---|
| category | #photos | #movies
time | example |
|---|
| Raccoon | 81 | 12
26:27 |
|
| Raccoon Dog | 5 | 1
01:39 |
|
| Badger | 5 | 1
01:51 |
|
| Fox | 6 | 1
01:29 |
|
| Masked Palm Civet | 5 | 1
01:47 |
|
- Download from here.
- Caffe LMDB data is generated
by convert_imageset.bin
without resize and grayscale options.
- The images are taken by Reconfigurable Systems Lab, Ritsumeikan University
supported by ...
- This project is a part of Institute of Disaster Mitigation for Urban Cultural Heritages ( r-dmuch.jp ),
Ritsumeikan University.
- For more details, please refer to ...
- 初田慎弥, 大野真史, 孟林, 泉知論,
"獣害対策のための監視カメラ向けアライグマ検出器の構築と評価",
画像電子学会誌,vol.48,no.2,pp.237-247,May 2019.
- 宮下洸大, 初田慎弥, 孟林, 泉知論,
"野生動物自動認識のためのデータベースの構築と深層学習の試行",
画像電子学会 第288回研究会予稿 18-03-12, pp.66-71, 徳島大学, 2019.3.2
- 初田慎弥, 大野真史, 泉知論, 孟林, "アライグマの自動検出と監視カメラ向け実装の試行", 画像電子学会第282回研究会予稿, 映像情報メディア学会技術報告 AIT2017-156, pp.25-28, Sep. 2017.
- 初田慎弥, 大野真史, 孟林, 泉知論, "害獣対策のためのアライグマ画像データベースの構築と深層学習による動物画像分類の試行", 電子情報通信学会 技術研究報告, vol.117, no.105, PRMU2017-34, pp.51-56, Jun. 2017.
Terms of Use
- Users of the database should respect the contributors to the database properly and avoid causing troubles on them.
- Images in the database provided here can be used freely for research and development which need raccoon images as long as the images themselves are not exposed. Statistical results for the images can be disclosed. If the database contributes to the result more than 20 percent in the number of images, the contribution should be acknowledged properly.
- Images themselves can be used only for non-profit academic activities in free-of-charge. Images can be exposed in academic papers (theses, articles, reports, etc.) with adequate acknowledgements. Non-target objects (including humans) accidentally taken in the images should not be exposed.