MacaquePose: A Novel 'In the Wild' Macaque Monkey Pose Dataset for Markerless Motion Capture
| Autor: | Rollyn Labuguen, Jumpei Matsumoto, Salvador Blanco Negrete, Hiroshi Nishimaru, Hisao Nishijo, Masahiko Takada, Yasuhiro Go, Ken-ichi Inoue, Tomohiro Shibata |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Cognitive Neuroscience ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Context (language use) non-human primate pose estimation behavior analysis Macaque Motion capture Motion (physics) lcsh:RC321-571 03 medical and health sciences Behavioral Neuroscience 0302 clinical medicine biology.animal Primate Computer vision Technology Report Pose lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry 030304 developmental biology large-scale dataset 0303 health sciences biology Artificial neural network business.industry Deep learning deep learning Neuropsychology and Physiological Psychology Artificial intelligence Spatiotemporal resolution business 030217 neurology & neurosurgery |
| Zdroj: | Frontiers in Behavioral Neuroscience, Vol 14 (2021) Frontiers in Behavioral Neuroscience |
| ISSN: | 1662-5153 |
| Popis: | Video-based markerless motion capture permits quantification of an animal's pose and motion, with a high spatiotemporal resolution in a naturalistic context, and is a powerful tool for analyzing the relationship between the animal's behaviors and its brain functions. Macaque monkeys are excellent non-human primate models, especially for studying neuroscience. Due to the lack of a dataset allowing training of a deep neural network for the macaque's markerless motion capture in the naturalistic context, it has been challenging to apply this technology for macaques-based studies. In this study, we created MacaquePose, a novel open dataset with manually labeled body part positions (keypoints) for macaques in naturalistic scenes, consisting of >13,000 images. We also validated the application of the dataset by training and evaluating an artificial neural network with the dataset. The results indicated that the keypoint estimation performance of the trained network was close to that of a human-level. The dataset will be instrumental to train/test the neural networks for markerless motion capture of the macaques and developments of the algorithms for the networks, contributing establishment of an innovative platform for behavior analysis for non-human primates for neuroscience and medicine, as well as other fields using macaques as a model organism. |
| Databáze: | OpenAIRE |
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