Multiple Monkey Pose Estimation Using OpenPose

Negrete, Blanco; Labuguen,; Matsumoto, Tadahiko; Go,; Inoue,; Shibata,

doi:10.1101/2021.01.28.428726

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…This dataset offers convenience and a widely accepted baseline for the subsequent research in this area. Salvador et al [12] used ResNet, which has achieved good performance in many visual tasks, for monkey pose estimation based on MacaquePose. The experimental results show that the pose estimation accuracy for monkeys has reached the same level as that for humans.…”

Section: Introductionmentioning

confidence: 99%

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

Fan

Liu

et al. 2022

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Macaque monkey is a rare substitute which plays an important role for human beings in relation to psychological and spiritual science research. It is essential for these studies to accurately estimate the pose information of macaque monkeys. Many large-scale models have achieved state-of-the-art results in pose macaque estimation. However, it is difficult to deploy when computing resources are limited. Combining the structure of high-resolution network and the design principle of light-weight network, we propose the attention-refined light-weight high-resolution network for macaque monkey pose estimation (HR-MPE). The multi-branch parallel structure is adopted to maintain high-resolution representation throughout the process. Moreover, a novel basic block is designed by a powerful transformer structure and polarized self-attention, where there is a simple structure and fewer parameters. Two attention refined blocks are added at the end of the parallel structure, which are composed of light-weight asymmetric convolutions and a triplet attention with almost no parameter, obtaining richer representation information. An unbiased data processing method is also utilized to obtain an accurate flipping result. The experiment is conducted on a macaque dataset containing more than 13,000 pictures. Our network has reached a 77.0 AP score, surpassing HRFormer with fewer parameters by 1.8 AP.

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Section: Introductionmentioning

confidence: 99%

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

Fan

Liu

et al. 2022

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Evaluating enrichment use in group-housed rhesus macaques (Macaca mulatta): A machine learning approach

Ciminelli,

Witham,

Bateson

2024

Anim. welf.

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Environmental enrichment programmes are widely used to improve welfare of captive and laboratory animals, especially non-human primates. Monitoring enrichment use over time is crucial, as animals may habituate and reduce their interaction with it. In this study we aimed to monitor the interaction with enrichment items in groups of rhesus macaques (Macaca mulatta), each consisting of an average of ten individuals, living in a breeding colony. To streamline the time-intensive task of assessing enrichment programmes we automated the evaluation process by using machine learning technologies. We built two computer vision-based pipelines to evaluate monkeys’ interactions with different enrichment items: a white drum containing raisins and a non-food-based puzzle. The first pipeline analyses the usage of enrichment items in nine groups, both when it contains food and when it is empty. The second pipeline counts the number of monkeys interacting with a puzzle across twelve groups. The data derived from the two pipelines reveal that the macaques consistently express interest in the food-based white drum enrichment, even several months after its introduction. The puzzle enrichment was monitored for one month, showing a gradual decline in interaction over time. These pipelines are valuable for assessing enrichment by minimising the time spent on animal observation and data analysis; this study demonstrates that automated methods can consistently monitor macaque engagement with enrichments, systematically tracking habituation responses and long-term effectiveness. Such advancements have significant implications for enhancing animal welfare, enabling the discontinuation of ineffective enrichments and the adaptation of enrichment plans to meet the animals’ needs.

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MacqD - A Deep Learning-based Model for Automatic Detection of Socially-housed Laboratory Macaques

Moat,

Gaudet-Trafit,

Paul

et al. 2024

Preprint

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Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. To address this gap, we present MacqD, a modified Mask R-CNN model incorporating a SWIN transformer backbone for enhanced attention-based feature extraction. MacqD robustly detects macaques in their home-cage under challenging scenarios, including occlusions, glass reflections, and overexposure to light. To evaluate MacqD and compare its performance against pre-existing macaque detection models, we collected and analysed video frames from 20 caged rhesus macaques at Newcastle University, UK. Our results demonstrate MacqD's superiority, achieving a median F1-score of 99% for frames with a single macaque in the focal cage (surpassing the next-best model by 21%) and 90% for frames with two macaques. Generalisation tests on frames from a different set of macaques from the same animal facility yielded median F1-scores of 95% for frames with a single macaque (surpassing the next-best model by 15%) and 81% for frames with two macaques (surpassing the alternative approach by 39% ). Finally, MacqD was applied to videos of paired macaques from another facility and resulted in F1-score of 90%, reflecting its strong generalisation capacity. This study highlights MacqD's effectiveness in accurately detecting macaques across diverse settings.

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Multiple Monkey Pose Estimation Using OpenPose

Cited by 4 publications

References 13 publications

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

An Attention-Refined Light-Weight High-Resolution Network for Macaque Monkey Pose Estimation

Evaluating enrichment use in group-housed rhesus macaques (Macaca mulatta): A machine learning approach

MacqD - A Deep Learning-based Model for Automatic Detection of Socially-housed Laboratory Macaques

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