دورية أكاديمية
OpenApePose, a database of annotated ape photographs for pose estimation
| العنوان: | OpenApePose, a database of annotated ape photographs for pose estimation |
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| المؤلفون: | Nisarg Desai, Praneet Bala, Rebecca Richardson, Jessica Raper, Jan Zimmermann, Benjamin Hayden |
| المصدر: | eLife, Vol 12 (2023) |
| بيانات النشر: | eLife Sciences Publications Ltd, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine LCC:Science LCC:Biology (General) |
| مصطلحات موضوعية: | apes, pose estimation, deep learning, behavior tracking, dataset, Medicine, Science, Biology (General), QH301-705.5 |
| الوصف: | Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose, a new public dataset of 71,868 photographs, annotated with 16 body landmarks of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans (COCO) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held-out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large, specialized databases for animal tracking systems and confirm the utility of our new ape database. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2050-084X |
| العلاقة: | https://elifesciences.org/articles/86873Test; https://doaj.org/toc/2050-084XTest |
| DOI: | 10.7554/eLife.86873 |
| الوصول الحر: | https://doaj.org/article/ec95565267dc4abf859dbdc0ca33b4a3Test |
| رقم الانضمام: | edsdoj.95565267dc4abf859dbdc0ca33b4a3 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2050084X |
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| DOI: | 10.7554/eLife.86873 |