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5 results for “pose-estimation”
replicAnt - Plum2023 - Pose-Estimation Datasets and Trained Models
<p>This dataset contains all recorded and hand-annotated as well as all synthetically generated data as well as representative trained networks used for semantic and instance segmentation experiments in the<em> replicAnt - generating annotated images of animals in complex environments using Unreal Engine</em> manuscript. Unless stated otherwise, all 3D animal models used in the synthetically generated data have been generated with the open-source photgrammetry platform scAnt <a href="http://peerj.com/articles/11155/">peerj.com/articles/11155/</a>. All synthetic data has been generated with the associated replicAnt project available from <a href="https://github.com/evo-biomech/replicAnt">https://github.com/evo-biomech/replicAnt</a>.</p> <p><strong>Abstract:</strong></p> <p>Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To overcome these limitations, we created replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware instead. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation; and that it increases the subject-specificity and domain-invariance of the trained networks, so conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.</p> <p><strong>Benchmark data</strong></p> <p>Two pose-estimation datasets were procured. Both datasets used first instar <em>Sungaya nexpectata</em> (Zompro 1996) stick insects as a model species. Recordings from an evenly lit platform served as representative for controlled laboratory conditions; recordings from a hand-held phone camera served as approximate example for serendipitous recordings in the field. </p> <p>For the platform experiments, walking <em>S. inexpectata</em> were recorded using a calibrated array of five FLIR blackfly colour cameras (Blackfly S USB3, Teledyne FLIR LLC, Wilsonville, Oregon, U.S.), each equipped with 8 mm c-mount lenses (M0828-MPW3 8MM 6MP F2.8-16 C-MOUNT, CBC Co., Ltd., Tokyo, Japan). All videos were recorded with 55 fps, and at the sensors’ native resolution of 2048 px by 1536 px. The cameras were synchronised for simultaneous capture from five perspectives (top, front right and left, back right and left), allowing for time-resolved, 3D reconstruction of animal pose.<br> <br> The handheld footage was recorded in landscape orientation with a Huawei P20 (Huawei Technologies Co., Ltd., Shenzhen, China) in stabilised video mode: <em>S. inexpectata </em>were recorded walking across cluttered environments (hands, lab benches, PhD desks etc), resulting in frequent partial occlusions, magnification changes, and uneven lighting, so creating a more varied pose-estimation dataset.<br> <br> Representative frames were extracted from videos using DeepLabCut (DLC)-internal k-means clustering. 46 key points in 805 and 200 frames for the platform and handheld case, respectively, were subsequently hand-annotated using the DLC annotation GUI.</p> <p><strong>Synthetic data</strong></p> <p>We generated a synthetic dataset of 10,000 images at a resolution of 1500 by 1500 px, based on a 3D model of a first instar <em>S. inexpectata </em>specimen, generated with the <a href="https://peerj.com/articles/11155/"><em>scAnt</em> photogrammetry workflow</a>. Generating 10,000 samples took about three hours on a consumer-grade laptop (6 Core 4 GHz CPU, 16 GB RAM, RTX 2070 Super). We applied 70\% scale variation, and enforced hue, brightness, contrast, and saturation shifts, to generate 10 separate sub-datasets containing 1000 samples each, which were combined to form the full dataset.</p> <p><strong>Funding</strong></p> <p>This study received funding from Imperial College’s President’s PhD Scholarship (to Fabian Plum), and is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 851705, to David Labonte). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p>
Early-life sleep disruption impairs subtle social behaviours in prairie voles: a pose-estimation study
<p><span>Early life sleep disruption (ELSD) has been shown to have long-lasting effects on social behaviour in adult prairie voles (Microtus ochrogaster), including impaired expression of pair bonding during partner preference testing. However, due to the limitations of manual behaviour tracking, the effects of ELSD across the time course of pair bonding have not yet been described, hindering our ability to trace mechanisms. Here, we used pose estimation to track prairie voles during opposite-sex cohabitation, the process leading to pair bonding. Male-female pairs were allowed to interact through a mesh divider in the home cage for 72 h, providing variables of body direction, distance-to-divider and locomotion speed. We found that control males displayed periodic patterns of body orientation towards females during cohabitation. In contrast, ELSD males showed reduced duration and ultradian periodicity of these body orientation behaviours towards females. Furthermore, in both sexes, ELSD altered spatial and temporal patterns of locomotion across the light/dark cycles of the 72-h recordings. This study allows a comprehensive behavioural assessment of the effects of ELSD on later life sociality and highlights subtle prairie vole behaviours. Our findings may shed light on neurodevelopmental disorders featuring sleep disruption and social deficits, such as autism spectrum disorders.</span></p>
Early-life sleep disruption impairs subtle social behaviours in prairie voles: a pose-estimation study
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Pose-estimated 3D data of infant spontaneous activity from Helsinki and Pisa
<p>This dataset contains pose-estimated 3D (and 2D proxy) data as well as trained models for generating infant Kinetic Age, collected from research trials in Helsinki and Pisa. The dataset is organized into separate archives for metadata, data streams, trained models, and predictions. Below is a detailed breakdown of the dataset contents:</p> <p><strong>Metadata</strong></p> <ul> <li><em>metadata/combined.csv</em><br> - test_id: Unique identifier for each infant.<br> - corrected_age: Corrected age of the infant in days.<br> - outcome: Neurodevelopmental outcome labels (0 typical, 1 weak impairment, 2 MNI).</li> </ul> <p><strong>Data</strong></p> <ul> <li><em>data/features.csv</em><br> - Handcrafted movement features computed for each, by experts annotated, useful video segment.</li> <li><em>data/streams/combined/*.feather</em><br> - 3D recording segment, with 18 J, B, V, A streams over 600 time steps.</li> <li><em>data/streams_2d/combined/*.feather </em><br> - 2D recording segment, with 18 J, B, V, A streams over 600 time steps.</li> </ul> <p><strong>Results</strong></p> <ul> <li><em>results/model/fold_n/...</em><br> - train_predictions.npy: Predictions made on the training set.<br> - val_predictions.npy: Predictions made on the validation set.<br> - best_model.ckpt: Checkpoint file containing the saved model weights.<br> - metadata.json: Metadata describing the fold, including training parameters and validation segments.<br> - scatter_*.png: Regression results.</li> </ul> <p><strong>Predictions</strong></p> <ul> <li><em>predictions/jb-aagcn-coord-xy_predictions.csv</em><br> - Model predictions for the 2D model on MNI samples.</li> <li><em>predictions/jb-aagcn-coord_predictions.csv</em><br> - Model predictions for the 3D model on MNI samples.</li> </ul>
Data set for "Pose-estimation methods for underactuated cable-driven parallel robots"
<p>See the attached readme file</p>
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