Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
198
datasets available to search
ShareScore release 0.9.0
Dataset results
198 results for “poses”
SEENIC: dataset for Spacecraft posE Estimation with NeuromorphIC vision
<p>Dataset used in the paper "Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing" (<a href="https://doi.org/10.48550/arXiv.2209.11945">arXiv</a>, <a href="https://ieeexplore.ieee.org/document/10160531">IEEE Xplore</a>), for the purpose of satellite pose estimation with an event camera.</p> <p>Both events and ground truth camera poses were captured across the 20 scenes in total. There are two trajectories, five lighting configurations and two camera speeds. All combinations of trajectory type, speed and lighting configuration were enumerated for capture. Sample event frames and dataset statistics are available in the paper linked above, along with our pose estimation method used on this dataset.</p> <p> </p> <p>Live-capture scene names use the following encoding: {satellite model}-{trajectory}-{speed}-{lighting configuration}</p> <p>The calibration scene (calibration.tar.gz) includes multiple views of a chessboard used to calibrate the camera intrinsics and extrinsics for the live-capture scenes. Camera parameters calibrated using this scene can be found in the <strong>calib.txt</strong> file, with the format: fx fy cx cy k1 k2 p1 p2 k3.</p> <p> </p> <p>All <strong>live-capture</strong> scenes have the same data format:</p> <p>scene/</p> <p> poses/ -- Raw timestamped robot gripper to base transforms</p> <p> cam-poses.csv -- Ground truth camera poses with the format {timestamp, Rx, Ry, Rz, x, y, z}</p> <p> events.csv -- Event stream with the format {timestamp, x, y, polarity (0=off, 1=on)}</p> <p> meta.json -- Metadata file with camera frame dimensions</p> <p>Note: all timestamps are in microseconds.</p> <p> </p> <p>The <strong>synthetic</strong> scene (synthetic.tar.gz) has the following data format:</p> <p>synthetic/</p> <p> poses/ -- Sequential poses captured at a constant time interval</p> <p> events.txt -- Event stream with the format: time (float s), x, y, polarity (0=off, 1=on) as specified at <a href="https://rpg.ifi.uzh.ch/davis_data.html">https://rpg.ifi.uzh.ch/davis_data.html</a></p> <p> camera_intrinsics.txt -- The camera intrinsic matrix (space separated)</p> <p>Note: please refer to the paper referenced below for further details on using this synthetic scene.</p> <p> </p> <p><strong>When using the data in an academic context, please cite the following paper.</strong></p> <pre>@INPROCEEDINGS{10160531, author={Jawaid, Mohsi and Elms, Ethan and Latif, Yasir and Chin, Tat-Jun}, booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, title={Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing}, year={2023}, volume={}, number={}, pages={11866-11873}, keywords={Adaptation models;Satellites;Pose estimation;Lighting;Robot sensing systems;Robustness;Data models}, doi={10.1109/ICRA48891.2023.10160531} }</pre>
Time Series Data of Gaze, Head Pose, Hand Pose, and Object Positions for Object Approaches with a Given Intention
<p>This data set comprises time series data of gaze, head pose, hand pose, and object positions for object approaches with a given intention. The data was captured in the context of the following publication:</p> <ul> <li><em>Michael Fennel, Serge Garbay, Antonio Zea, Uwe D. Hanebeck</em>, <strong>Intention Estimation with Recurrent Neural Networks for Mixed Reality Environments</strong>, Proceedings of the 26th International Conference on Information Fusion (Fusion 2023) <em>(under review)</em></li> </ul> <p>A Microsoft Hololens 2 was used for recording the data at 60 fps under the modalities explained in detail in the above-mentioned paper.</p> <p>The file names are structured as follows:</p> <ul> <li><em>1st/2nd:</em> <ul> <li>The data with "1st" contains approaches to randomly placed objects on a grid, which are rendered in augmented reality. The user is informed about the object to approach using a visual cue. This corresponds to Section IV-A.</li> <li>The data with "2nd" contains approaches to real objects placed statically in a room. The user is informed about the object to approach using a voice command.</li> </ul> </li> <li><em>unfiltered:</em> Contains all approaches, including those where the user disrespects the given commands. Filtering is done as described in the paper.</li> <li><em>train/val/test:</em> The first dataset was split in a 70/20/10 ratio for training, validation, and test.</li> </ul> <p>Each data set contains the following columns. In each approach, 5 objects numbered from i=0 to i=4 are present.</p> <ul> <li>General: <ul> <li><em>time:</em> in seconds</li> <li><em>subject:</em> consecutive subject number</li> <li><em>handedness:</em> left (1), right (0)</li> <li><em>trial:</em> consecutive trial number per subject</li> <li><em>target_label:</em> index of the object to approach (0 to 4)</li> </ul> </li> <li>Data in world coordinates: <ul> <li><em>head_{x,y,z}:</em> head position</li> <li><em>head_quat_{w,x,y,z}:</em> head orientation quaternion</li> <li><em>W_gaze_{x,y,z}:</em> gaze direction</li> <li><em>W_r_hand_{x,y,z}:</em> right hand position</li> <li><em>W_r_hand_quat_{w,x,y,z}:</em> right hand orientation quaternion</li> <li><em>W_l_hand_{x,y,z}:</em> left hand position</li> <li><em>W_l_hand_quat_{w,x,y,z}:</em> left hand orientation quaternion</li> <li><em>W_object_i_{x,y,z}:</em> position of object i</li> <li><em>W_object_i_quat {w,x,y,z}</em>: orientation quaternion of object i</li> </ul> </li> <li>Data in egocentric coordinates (head coordinate system). This data is provided for convenience and can be derived from the other data: <ul> <li><em>gaze_{x,y,z}:</em> gaze direction</li> <li><em>r_hand_{x,y,z}:</em> right hand position</li> <li><em>r_hand_quat_{w,x,y,z}:</em> right hand orientation quaternion</li> <li><em>l_hand_{x,y,z}:</em> left hand position</li> <li><em>l_hand_quat_{w,x,y,z}:</em> left hand orientation quaternion</li> <li><em>object_i_{x,y,z}:</em> position of object i</li> <li><em>object_i_quat {w,x,y,z}</em>: orientation quaternion of object i</li> </ul> </li> </ul> <p><strong>Acknowledgment:</strong></p> <p>This work was supported by the <a href="https://robdekon.de/">ROBDEKON</a> project of the German Federal Ministry of Education and Research.</p>
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>
TOP-100 DOCKING POSES OF FDA APPROVED DRUGS AND DRUGS IN CLINICAL INVESTIGATION AT SARS-CoV2 MAIN PROTEASE
<p>7922 compounds were downloaded from NPC database (https://tripod.nih.gov/npc/). In order<br> to eliminate the non-specific binders, some criteria including molecular weight, between 100 to<br> 1000 g/mol; number of rotatable bonds, <100; number of atoms, between 10 and 100; number<br> of aliphatic and aromatic rings, <10; number of hydrogen-bond acceptor and donors, <10 were<br> set and as a result the total number of compounds was decreased to 6654. These ligands were<br> prepared using LigPrep module of Maestro at neutral pH (LigPrep, Schrodinger v.2017). In<br> molecular docking, we used following protein structure: SARS-CoV2 Main Protease, (PDB, 6LU7). The protein<br> was prepared using Protein Preparation module of Maestro. PROPKA was used for<br> determination of protonation states of amino acid residues. Restrained minimization was<br> performed with OPLS3 force field for the protein using 0.3 Å heavy atom convergence.<br> Docking was performed with Glide/SP using default settings. Top-100 docking poses were provided.</p>
TOP-100 DOCKING POSES OF FDA APPROVED AND DRUGS IN CLINICAL INVESTIGATION AT SARS-CoV2 SPIKE/ACE2 INTERFACE
<p>7922 compounds were downloaded from NPC database (https://tripod.nih.gov/npc/). In order<br> to eliminate the non-specific binders, some criteria including molecular weight, between 100 to<br> 1000 g/mol; number of rotatable bonds, <100; number of atoms, between 10 and 100; number<br> of aliphatic and aromatic rings, <10; number of hydrogen-bond acceptor and donors, <10 were<br> set and as a result the total number of compounds was decreased to 6654. These ligands were<br> prepared using LigPrep module of Maestro at neutral pH (LigPrep, Schrodinger v.2017). In<br> molecular docking, we used following protein structure: Spike Protein/ACE-2, (PDB, 6M0J). The protein<br> was prepared using Protein Preparation module of Maestro. PROPKA was used for<br> determination of protonation states of amino acid residues. Restrained minimization was<br> performed with OPLS3 force field for the protein using 0.3 Å heavy atom convergence.<br> Docking was performed with Glide/SP using default settings. Top-100 docking poses were provided.</p>
dataset for lateral posing
<p>This is zip archive for dataset for lateral posing experiments. See readme.txt for more information. </p>
Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the rendered images for HO3Dv2.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 rendered images.</div> <div>├── <a href="../api/records/13228003/draft/files/train_ho3d.zip/content" target="_blank" rel="noopener noreferrer">train_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 training set.</div> <div>├── <a href="../api/records/13228003/draft/files/full_test_dexycb.zip/content" target="_blank" rel="noopener noreferrer">full_test_dexycb.zip</a> - Contains the processed SDF files for DexYCB full test set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Processed data and trained models for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: <a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf">https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</a></p> <p>Link to the Arxiv article: <a href="https://arxiv.org/abs/2402.17062">https://arxiv.org/abs/2402.17062</a></p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed data of the interacting objects and SDF samples. Meanwhile, we also include the trained model weights here.</div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/11668766/draft/files/ckpts.zip/content" target="_blank" rel="noopener noreferrer">ckpts.zip</a> - Contains the trained weights model on different datasets (DexYCB and HO3Dv2)</div> <div>├── <a href="../api/records/11668766/draft/files/annotations.zip/content" target="_blank" rel="noopener noreferrer">annotations.zip</a> - Contains the preprocessed annotations of DexYCB and HO3Dv2 for efficient data loading.</div> <div>├── <a href="../api/records/11668766/draft/files/simple_ycb_models.zip/content" target="_blank" rel="noopener noreferrer">simple_ycb_models.zip</a> - Contains the preprocessed YCB objects for batched evaluation.</div> <div>├── <a href="../api/records/11668766/draft/files/test.zip/content" target="_blank" rel="noopener noreferrer">test.zip</a> - Contains the processed SDF files for DexYCB test set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set.</div> <div>├── <a href="https://zenodo.org/api/records/14190951/draft/files/ho3d_render_release.zip/content" target="_blank" rel="noopener noreferrer">ho3d_render_release.zip</a> - Contains the HO3Dv2 submission trained with HO3D training set and rendering set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: <a href="https://github.com/amathislab/HOISDF">https://github.com/amathislab/HOISDF</a></div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Aschaffenburg Pose Dataset
<p>This dataset contains trajectories as well as body poses of pedestrians and cyclists in road traffic recorded in Aschaffenburg, Germany. It is appropriate for training and testing methods for trajectory forecasting and intention prediction of vulnerable road users (VRUs) based on the past trajectory and body poses.</p> <p>The dataset consists of more than 6526 trajectories of pedestrians and 1734 trajectories of cyclists recorded by a research vehicle of the University of Applied Sciences Aschaffenburg (<a href="https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/">Kooperative Automatisierte Verkehrssysteme</a>) in urban traffic. The trajectories have been measured with the help of a stereo camera while compensating the vehicle's own motion. The body posture of the pedestrians and cyclists is available in the form of 2D and 3D poses. The 2D poses contain joint positions in an image coordinate system, while the 3D poses contain actual three-dimensional positions. A detailed description and evaluation of the pose estimation method can be found in [1]. In addition to the trajectories and the poses, manually created labels of the respective motion states are included.</p> <p>To read the provided data, unzip the file first. It contains one json file for each of the trajectories. Each json file contains the following data:</p> <ul> <li><strong>vru_type</strong>: type of the VRU (pedestrian ('ped') or cyclist ('bike'))</li> <li><strong>timestamps</strong>: UTC-Timestamps. The motions of the VRUs were recorded at a frequency of 25 Hz.</li> <li><strong>set</strong>: Assignment to one of the three datasets train, validation or test. For pedestrians and cyclists, 60% of the data is used for training, 20% for validation and the remaining 20% for testing. During all splits, it was ensured that the distribution of the motion states is as similar as possible.</li> <li><strong>pose2d</strong>: 2D poses with 18 joint positions in image coordinates with an additional uncertainty between 0 and 1 (third coordinate). Missing positions are encoded as 'nan'.</li> <li><strong>pose3d</strong>: 3D poses with the trajectories of 14 joints in an three dimensional coordinate system. Missing positions are encoded as 'nan'.</li> <li><strong>head_smoothed</strong>: Smoothed (by rts smoother) trajectory of the head in an three dimensional coordinate system. It is treated as ground truth position and must not be used as input for a prediction method.</li> <li><strong>motion_primitives</strong>: One-hot encoded labels of the respective motion state. For pedestrians, a distinction is made between the states wait, start, move, and stop. For cyclists, the states wait, start, move, stop, turn left, and turn right are annotated.</li> </ul> <p>Python code for reading the data can be found on Github: <a href="https://github.com/CooperativeAutomatedTrafficSystemsLab/Aschaffenburg-Pose-Dataset">github.com/CooperativeAutomatedTrafficSystemsLab/Aschaffenburg-Pose-Dataset</a></p> <p> </p> <p><strong>Citation</strong></p> <p>If you find this dataset useful, please cite this paper (and refer the data as Aschaffenburg Pose Dataset or APD):</p> <p>Kress, V. ; Zernetsch, S. ; Doll, K. ; Sick, B. : Pose Based Trajectory Forecast of Vulnerable Road Users Using Recurrent Neural Networks. In: Pattern Recognition. ICPR International Workshops and Challenges, Springer International Publishing, 2020, pp. 57-71<br> </p> <p><strong>Similar Datasets</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4898838">Pedestrians and Cyclists in Road Traffic: Trajectories, 3D Poses and Semantic Maps</a></li> <li><a href="https://doi.org/10.5281/zenodo.3734038">Cyclist Actions: Optical Flow Sequences and Trajectories</a></li> <li><a href="https://doi.org/10.5281/zenodo.4080940">Cyclist Actions: Motion History Images and Trajectories</a></li> <li><a href="https://www.h-ab.de/vru-trajectory-dataset">More datasets</a></li> </ul> <p> </p> <p><strong>Acknowledgment</strong></p> <p>This work was supported by “Zentrum Digitalisierung.Bayern”. In addition, the work is backed by the project DeCoInt2 , supported by the German Research Foundation (DFG) within the priority program SPP 1835: “Kooperativ interagierende Automobile”, grant numbers DO 1186/1-2 and SI 674/11-2.</p> <p> </p> <p><strong>References</strong></p> <p>[1] Kress, V. ; Jung, J. ; Zernetsch, S. ; Doll, K. ; Sick, B. : Human Pose Estimation in Real Traffic Scenes. In: IEEE Symposium Series on Computational Intelligence (SSCI), 2018, pp. 518–523, doi: 10.1109/SSCI.2018.8628660</p> <p> </p>
DoPose: dataset for object segmentation and 6D pose estimation
<p>DoPose (Dortmund Pose)is a dataset of highly cluttered and closely stacked objects. The dataset is saved in the <a href="https://github.com/thodan/bop_toolkit/blob/master/docs/bop_datasets_format.md">BOP format</a>. The dataset includes RGB images, Depth images, 6D Pose of objects, segmentation mask (all and visible), COCO Json annotation, camera transformations, and 3D model of all objects. The dataset contains 2 different types of scenes (table and bin). Each scene contains different view angles. For the bin scenes, the data contains 183 scenes with 2150 image views. In those 183 scenes 35 scenes contain 2 views, 20 contains 3 views and 128 contains 16 views. And for table scenes, the data contains 118 scenes with 1175 image views. in Those 118 scenes, 20 scenes contain 3 views, 50 scenes with 6 images, and 48 scenes with 17 images. So in total, our data contains 301 scenes and 3325 view images. Most of the scenes contain mixed objects. The dataset contains 19 objects in total.</p> <p>For more info about the dataset content and collection process please refer to our <a href="https://arxiv.org/abs/2204.13613">Arxiv preprint</a></p> <p>If you have any questions about the dataset, please contact <strong>anas.gouda@tu-dortmund.de</strong></p>
Thermal demagnetization data of Risica et al. (Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala)
<p>Thermal demagnetization data (repository data) of Risica et al. "Deposit-derived block-and-ash flows: the hazard posed by perched temporary tephra accumulations on volcanoes; 2018 Fuego disaster, Guatemala".</p>
Complete Inertial Pose (CIP) Dataset
<pre>Contains the <strong>Complete Inertial Pose (CIP)</strong> <strong>Dataset</strong>: 1) <strong>Ergowear</strong>: acquisition using custom Ergowear wearable system (9 low-cost MARG sensors - MPU9250) and custom software. Contains ~4.17M@100Hz samples of data. 2) <strong>MTwAwinda</strong>: acquisition using XsensAwinda Mocap system (17 high-end MARG sensors - MTw) and the XsensMtManager software. Contains ~1.0M@60Hz samples of data. Both systems contain synchronized GT data from a commercial MoCap system (XsensAwinda Hardware + XsensAnalyse Software), sampled at 60Hz. </pre> <p>Usage: Follow readme.md on the "code" folder.</p> <p>Updated code, with extended functionality can be accessed on <a href="https://github.com/ManuelPalermo/HumanInertialPose.git">github.com/ManuelPalermo/HumanInertialPose</a>.</p>
Validating marker-less pose estimation with 3D x-ray radiography
<p class="MsoNormal"><span>These data were generated to evaluate the accuracy of DeepLabCut (DLC), a deep learning marker-less motion capture approach, by comparing it to a 3D x-ray video radiography system that tracks markers placed under the skin (XROMM). We recorded behavioral data simultaneously with XROMM and RGB video as marmosets foraged and reconstructed three-dimensional kinematics in a common coordinate system. We used XMALab to track 11 XROMM markers, and we used the toolkit Anipose to filter and triangulate DLC trajectories of 11 corresponding markers on the forelimb and torso. We performed a parameter sweep of relevant Anipose and post-processing parameters to characterize their effect on tracking quality. We compared the median error of DLC+Anipose to human labeling performance and placed this error in the context of the animal's range of motion. </span></p>
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p> <p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p> <p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p> <p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p> <p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p> <p>To load these files into your Python environment, you can use the following code snippet:</p> <div> <div>th4figs = '/content/drive/MyDrive/compag_2023/'</div> <br> <div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div> <div>Xtrain = np.load(path4images)</div> <br> <div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div> <div>Ytrain = np.load(path4labels).astype(float)</div> <br> <div>print("X train : ", Xtrain.shape)</div> <div>print("Y train : ", Ytrain.shape)</div> <div> <div> <div> <div> <div> <div> <div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <pre>X train : (5332, 300, 300, 3) Y train : (5332,)<br> </pre> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Pose Selector Workflow - Structure Input Files for Machine Learning (Set 2)
<p>Second set of structure input files for the docking poses of the remaining 2022 protein-ligand complexes. Together with the structure files and absolute binding free energy (ABFE) estimates shared in 10.5281/zenodo.11397017, this data can be used to train a machine-learning (ML) model predicting the ABFE of binding poses of protein-ligand complexes.</p> <p>More background on the workflow generating the structure files and ABFE estimates is provided in 10.5281/zenodo.11397017.</p>
Pose Selector Workflow - Docking Poses, Absolute Binding Free Energy Estimates and Structure Input Files for Machine Learning
<p>The Pose Selector (PS) workflow calculates absolute binding free energies (ABFEs) for binding poses of protein-ligand complexes. First, it converts the binding poses (both docking poses as well as experimentally observed ligand binding poses), which are provided as a combination of protein PDB file and ligand MOL2 file, into input files for molecular dynamics (MD) simulations with GROMACS after they have passed extensive quality checks and repair steps. Next, the PS workflow post-processes and analyses the last frame of the resulting eight 100 ps trajectories per binding pose with the Generalised Born model of implicit solvation as implemented in gmx_MMPBSA to obtain the ABFE estimates. The workflow was designed for soluble proteins without post-translational modifications, co-factors and non-standard amino acids, and it has limited support for coordinated ions.</p> <p>For the dataset published here, the PS workflow was run on docking poses generated for the PDBbind 2020 dataset (http://www.pdbbind.org.cn/index.php), shared in dockingPosesPDBBind2020.tar.gz. This entry and its partner entry 10.5281/zenodo.11397486 also share the intial coordinates used in the MD simulations of >800,000 docking poses of 4022 protein-ligand complexes (structureFiles_dockingPoses1.tar.gz in this entry and structureFiles_dockingPoses2.tar.gz in 10.5281/zenodo.11397486) and of the experimental ligand binding pose of 4549 complexes (structureFiles_experimentalStructures.tar.gz) as well as the corresponding ABFE estimates (absoluteBindingFreeEnergyEstimates.tar.gz). The MD simulations were run on the LUMI and MeluXina supercomputers while the implicit-solvent calculations were carried out on Galileo (Cineca).</p> <p>The README file describes the structure of the shared data in more detail and points out how to reproduce the MD trajectories and the subsequent implicit-solvent calculations yielding the free-energy estimates as well as how to use the data provided in this entry to train a machine-learning model predicting the ABFE of binding poses of protein-ligand complexes. The workflow scripts can be downloaded from GitHub (https://github.com/LigateProject/Pose-Selector-workflow). The MD simulations were run with GROMACS 2023.2 (https://manual.gromacs.org/2023.2/index.html), and the implicit-solvent calculations were carried out with gmx_MMPBSA 1.6.1 (https://valdes-tresanco-ms.github.io/gmx_MMPBSA/v1.6.1/).</p>
Total Capture's MediaPipe Human Pose Landmarks
<p>The dataset contains the human pose (world) landmarks that have been extracted with the MediaPipe Pose Estimator from the Total Capture dataset videos. The dataset only contains the landmarks of the first camera (camera 1) videos. MediaPipe’s “Heavy” version (model complexity of ‘two’) has been used during the process. </p> <p>The dataset was funded by the Horizon Europe Programme under Grant agreement N. 101092612 (Social and hUman ceNtered XR - SUN project).</p>
Fig. 1 in Invasion risks posed by ornamental freshwater fish trade to southeastern Brazilian rivers
Fig. 1. Cities and watersheds within the Minas Gerais State, Brazil, where the 39 ornamental fish stores were visited.
Fig. 2. A in Invasion risks posed by ornamental freshwater fish trade to southeastern Brazilian rivers
Fig. 2. A model describing the invasion stages that species must pass in order to represent an invasion risk for rivers in Minas Gerais State, Brazil.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.