ArticleFrontiers in cellular neuroscience2021
OptiFlex: Multi-Frame Animal Pose Estimation Combining Deep Learning With Optical Flow.
Article in Frontiers in cellular neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Does advancement in marker-less pose-estimation mean more quality research? A systematic review.Frontiers in behavioral neuroscience · 2025Pooled it
- Deep-learning-based markerless tracking of distal anatomical landmarks in clinically recorded videos for assessing infant movement patterns associated with neurodevelopmental status.Journal of the Royal Society of New Zealand · 2025Article
- The modified elevated gap interaction test: a novel paradigm to assess social preference.Open biology · 2024Article
- Rapid automated 3-D pose estimation of larval zebrafish using a physical model-trained neural network.PLoS computational biology · 2023Article
- Captive Animal Behavior Study by Video Analysis.Sensors (Basel, Switzerland) · 2023Article
- Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision.International journal of computer vision · 2023Article
- Functional trajectories during innate spinal cord repair.Frontiers in molecular neuroscience · 2023Article
- Estimation of skeletal kinematics in freely moving rodents.Nature methods · 2022Article
- Multicow pose estimation based on keypoint extraction.PloS one · 2022Article
- Marker-Less Motion Capture of Insect Locomotion With Deep Neural Networks Pre-trained on Synthetic Videos.Frontiers in behavioral neuroscience · 2021Article
- OptiFlex: Multi-Frame Animal Pose Estimation Combining Deep Learning With Optical Flow.Frontiers in cellular neuroscience · 2021Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Animal pose estimation tools based on deep learning have greatly improved animal behaviour quantification. These tools perform pose estimation on individual video frames, but do not account for variability of animal body shape in their prediction and evaluation. Here, we introduce a novel multi-frame animal pose estimation framework, referred to as OptiFlex. This framework integrates a flexible base model (i.e., FlexibleBaseline), which accounts for variability in animal body shape, with an OpticalFlow model that incorporates temporal context from nearby video frames. Pose estimation can be optimised using multi-view information to leverage all four dimensions (3D space and time). We evaluate FlexibleBaseline using datasets of four different lab animal species (mouse, fruit fly, zebrafish, and monkey) and introduce an intuitive evaluation metric-adjusted percentage of correct key points (aPCK). Our analyses show that OptiFlex provides prediction accuracy that outperforms current deep learning based tools, highlighting its potential for studying a wide range of behaviours across different animal species.
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