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1,506 results for “objects”
Crowd simulation (CrowdSim2) for tracking and object detection
<p>CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars). </p> <table align="center"> <caption>Information summarizing number of folders, seconds and frames of data for different weather conditions</caption> <thead> <tr> <th scope="col">Condition</th> <th scope="col">Folders</th> <th scope="col">Seconds</th> <th scope="col">Frames</th> </tr> </thead> <tbody> <tr> <td>Sun</td> <td>2899</td> <td>86 970</td> <td>2 174 250</td> </tr> <tr> <td>Rain</td> <td>1633</td> <td>48 990</td> <td>1 224 750</td> </tr> <tr> <td>Fog</td> <td>1653</td> <td>49 590</td> <td>1 239 750</td> </tr> <tr> <td>Snow</td> <td>1646</td> <td>49 380</td> <td>1 234 500</td> </tr> </tbody> </table> <p>Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: <a href="https://crowdsim.aei.polsl.pl/">CrowdSim</a></p> <p><strong>Acknowledgments</strong> </p> <p>This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk. </p> <p> </p> <p><strong>Citing the Crowdsim 2</strong></p> <p>The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form.<br> Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people</p> <pre><code class="language-markdown">TBA: Article citations using this dataset will appear here</code></pre> <p>and this Zenodo Dataset</p> <pre><code class="language-markdown">@dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }</code></pre> <p> </p>
Data from: Adaptive multi-objective control explains how humans make lateral maneuvers while walking
<p>To successfully traverse their environment, humans often perform maneuvers to achieve desired task goals while simultaneously maintaining balance. Humans accomplish these tasks primarily by modulating their foot placements. As humans are more unstable laterally, we must better understand how humans modulate lateral foot placement. We previously developed a theoretical framework and corresponding computational models to describe how humans regulate lateral stepping during straight-ahead continuous walking. We identified goal functions for step width and lateral body position that define the walking task and determine the set of all possible task solutions as Goal Equivalent Manifolds (GEMs). Here, we used this framework to determine if humans can regulate lateral stepping during non-steady-state lateral maneuvers by minimizing errors consistent with these goal functions. Twenty young healthy adults each performed four lateral lane-change maneuvers in a virtual reality environment. Extending our general lateral stepping regulation framework, we first re-examined the requirements of such transient walking tasks. Doing so yielded new theoretical predictions regarding how steps during any such maneuver should be regulated to minimize error costs, consistent with the goals required at each step and with how these costs are adapted at each step during the maneuver. Humans performed the experimental lateral maneuvers in a manner consistent with our theoretical predictions. Furthermore, their stepping behavior was well modeled by allowing the parameters of our previous lateral stepping models to adapt from step to step. To our knowledge, our results are the first to demonstrate humans might use evolving cost landscapes in real time to perform such an adaptive motor task and, furthermore, that such adaptation can occur quickly – over only one step. Thus, the predictive capabilities of our general stepping regulation framework extend to a much greater range of walking tasks beyond just normal, straight-ahead walking.</p>
Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds. II: Spectroscopic Data of Thorne-Zytkow Object and Super-AGB Star Candidates
<p>This dataset contains Magellan MIKE spectroscopy of a population of cool, luminous stars in the Magellanic Clouds, a sample of confirmed Magellanic Cloud red supergiants, and spectrophotometric standard stars. The spectra were analyzed in the paper "Cool, Luminous, and Highly Variable Stars in the Magellanic Clouds. II: Spectroscopic and Environmental Analysis of Thorne-\.Zytkow Object and Super-AGB Star Candidates" by O'Grady et al. (2022). More details are provided in the README. </p>
Image Dataset for Object Detection of Small Size Construction Tools
<p> This is an image dataset established as input data for object detection model of small-sized construction tools. In the dataset, there are 12 classes of target tools (bucket, cutter, drill, grinder, hammer, knife, saw, shovel, spanner, tacker, trowel, and wrench) which are typically used at indoor construction sites. 25,084 sets of image and the corresponding label data have been established and shared. </p> <p> The diversity of objects in the images of the 12 small tools was considered by photographing tools of various shapes, sizes, and colors. In addition, to improve the model performance, images were also captured with various changes (e.g., image resolution, occlusion, lighting, and background). Among the 25,084 images in the dataset, 6,258 (25%) were obtained from the actual construction site. </p> <p> Object annotations in each image were done by bounding boxes and were saved into a text file. The coordinates of the bounding box have the form of (Class, Center X, Center Y, Width, Height). Class refers to one of 12 construction tool types. Center X and Center Y are the center coordinates of the bounding box for an object from an image when the resolution of the image has min-max normalized. Width and Height are the width and height of the bounding box for an object, respectively, also from the image with the min-max normalized resolution.</p> <p> </p> <p>The peer-reviewed publication for this dataset has now been published in " KSCE Journal of Civil Engineering" a Springer journal as follows:</p> <p><strong>* Lee, K., Jeon, C., and Shin, D. (2023, In press) "Small Tool Image Database and Object Detection Approach for Indoor Construction Site Safety" <em>KSCE Journal of Civil Engineering</em>. DOI: https://doi.org/10.1007/s12205-022-1011-7</strong></p> <p>Please cite this reference when using the dataset.</p>
Experimental Results for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems"
<p>This repository contains the experiment results (raw data in NPZ and CSV format and Latex tables) for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems", which is accepted in <em>Mathematical and Computational Applications</em> journal.</p> <p>The preprint version of the related paper is already online: </p> <p> </p> <p>Wang, H.; Emmerich, M.; Deutz, A.; Hernández, V.A.S.; Schütze, O. The Hypervolume Newton Method for Constrained Multi-objective Optimization Problems. <em>Preprints</em> <strong>2022</strong>, 2022110103 (doi: <a href="http://10.20944/preprints202211.0103.v1">10.20944/preprints202211.0103.v1</a>).</p> <p><strong>Data description:</strong> we benchmarked<strong> </strong>three algorithms: (1) the standalone <strong>Hypervolume Netwon Method</strong> (HVN), (2) NSGA-III, and (3) the <strong>hybridization</strong> of the standalone HVN and NSGA-III on several artificial problems.</p> <ul> <li>For the standalone HVN algorithm, we tested it on three simple artificial test problems - P1, P2, and P3 (proposed in the above paper): <ul> <li>2D-example-50*.tex: problem P1</li> <li>3D-example1*.tex: problem P2</li> <li>3D-example2*.tex: problem P3</li> </ul> </li> <li>For NSGA-III and the hybridization, we tested them on the equality-constrained DTLZ and Inverted DTLZ (IDTLZ) problems: <ul> <li>Eq1DTLZ.*npz: DTLZ problems</li> <li>Eq1IDTLZ*.npz: IDTLZ problems</li> </ul> </li> </ul>
Dataset of Public Objects in Uncontrolled Environment for Navigation Aiding
<p>Computer vision is a new approach to navigation aiding that assist visually impaired people to travel independently. A deep learning-based solution implemented on a portable device that uses a monocular camera to capture public objects could be a low-cost and handy navigation aid. By recognizing public objects in the street and estimating the distance between the users, visually impaired people are able to avoid obstacles in the outdoor environment and walk safely. This dataset is created for navigation aiding and contains 3 classes of objects which commonly exist on the pavement in the city. It is verified that the dataset is of high quality for object detection and distance estimation, and is ultimately utilized for navigation aid solution.</p>
RTI simulated imagery - Coin-like object with and without surface markings
<p>A set of simulated reflectance transformation imaging (RTI) acquisitions using the 3D model of a coin-like object. One set is simulated with the base model and the other is with some markings created on the surface. The images were simulated using Blender.</p> <p>Each acquisitions contained the simulated RGB images as well as depth maps and normal maps computed by Blender using the 3D model. Also included are the .blend files used for the simulations which include the 3D models. There is also a .lp file which describes the light positions used.</p>
HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
<p>Add citation file.</p>
VISION Invited lecture - Objective: Personalised Prevention of Pancreatic Cancer
<p>Recording of the invited lecture that took place online on 16 December 2021 - <strong>Núria Malats, MD, MPH, PhD - Objective: Personalised Prevention of Pancreatic Cancer.</strong></p> <p>Identifying the population at high risk of developing non-hereditary pancreatic cancer in order to offer them to enter screening programs and diagnose cancer in early stages, is a must. To this end, we integrate information on the risk factors for this tumor with biomarkers determined at the omics level, including genomics and microbiome. All this information comes from a large study carried out in 28 centres from six European countries. For the integration of this big molecular data with epidemiological and clinical data, we use artificial intelligence tools. In the lecture, I will go through the most recent achievements of these collective efforts.</p>
Synaptic gradients transform object location to action: data and outputs
<p>Data an outputs associated with the following article:</p> <p>Dombrovski, M., Peek, M.Y., Park, JY. <em>et al.</em> Synaptic gradients transform object location to action. <em>Nature (2023)</em>. <a href="https://doi.org/10.1038/s41586-022-05562-8">https://doi.org/10.1038/s41586-022-05562-8</a></p> <p>and the following software:</p> <p><a href="https://github.com/avaccari/DrosophilaVPNWiring">https://github.com/avaccari/DrosophilaVPNWiring</a></p>
An Unique Dataset for Christian Sacral Objects Identification
<p>Christian religious monuments, such as cathedrals, chapels and temples are found in many places on our planet. World-famous buildings such as the Notre Dame Cathedral in Paris, Gaudi's Cathedral in Barcelona, and St. Vitus Cathedral in Prague are commonly known, and there are many photographs on the Internet that can be used to build machine learning models to identify them. For little known buildings such as small churches in the Czech-German border region, the number of photographs is already significantly lower and similar approaches cannot be used for identification. Based on these facts, our team has compiled an unique dataset for the identification of the most important elements of Christian sacral buildings such as altars, frescoes, pulpits, etc. which are almost always found in them. Our data set was manually created from several thousand real photographs. This dataset seems to be very usable, e.g., for creating new machine learning models and for identifying objects in sacred objects or the objects themselves.</p> <p>This dataset was created within the framework of the project Information system for medieval monuments in the Czech-Bavarian border area, No. 335, which is co-financed by the European Regional Development Fund and the state budget of the Czech Republic (Cross-border Cooperation Programme Czech Republic - Free State of Bavaria Objective ECA 2014-20).</p> <p> </p>
Case study for Sustainable agri-food supply chain planning through multi-objective optimisation
<p>Case study used to solve the multi-objective model proposed in: Sustainable agri-food supply chain planning through multi-objective optimisation</p>
Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters
Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 173219 quasi-random hyperparameter combinations were generated across 23 hyperparameters and used to train CrabNet on the Matbench experimental band gap dataset. The results were logged to a free-tier shared MongoDB Atlas dataset. This study resulted in a regression dataset mapping hyperparameter combinations (including repeats) to MAE, RMSE, computational runtime, and model size for CrabNet model trained on the Matbench experimental band gap benchmark task1. This dataset is used to create a surrogate model as close as possible to running the actual simulations by incorporating heteroskedastic noise. Failure cases for bad hyperparameter combinations were excluded via careful construction of the hyperparameter search space, and so were not considered as was done in prior work. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This contrasts with a more traditional approach that imposes a-priori assumptions such as Gaussian noise, e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
An intracochlear electrocochleography dataset: From raw data to objective analysis using deep learning
<p>Electrocochleography (ECochG) measures electrophysiological inner ear potentials in response to acoustic stimulation. These potentials reflect the state of the inner ear and provide important information about its residual function. For cochlear implant (CI) recipients, we can measure ECochG signals directly within the cochlea using the implant electrode. We are able to perform these recordings during and at any point after implantation.<br>However, the analysis and interpretation of ECochG signals are not trivial. To assist the scientific community, we provide our intracochlear ECochG data set, which consists of approximately 5,000 signals recorded from 46 ears with a cochlear implant. We collected data either immediately after electrode insertion or postoperatively in subjects with residual acoustic hearing. This data descriptor aims to provide the research community access to our comprehensive electrophysiological data set and algorithms. It includes all steps from raw data acquisition to signal processing and objective analysis using Deep Learning. In addition, we collected subject demographic data, hearing thresholds, subjective loudness levels, impedance telemetry, radiographic findings, and classification of ECochG signals.</p>
Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"
<p>This repository contains the dataset produced by the population-synthesis code SEVN for the paper:<br> "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"</p> <p>In this paper, we exploit the SEVN code (publicly available at <a href="https://gitlab.com/sevncodes/sevn">https://gitlab.com/sevncodes/sevn</a>) to analyse the formation and properties of binary compact objects.</p> <p>The repository also includes the initial condistions used as input and the SEVN version used to run the simulations.</p> <p>#Content</p> <p>The repository contains the following folders:</p> <p>- data_from_simulations: the folder contains all the data produced by the simulations and used in the Iorio+22 paper<br> - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper<br> - SEVN_iorio22: this folder contains the version of the SEVN code that has been used to run the simulations in the Iorio+22 paper</p> <p>Each folder contains a specific README with additional information</p> <p> </p> <p>#Contatcts</p> <p>giuliano.iorio.astro@gmail.com</p> <p> </p> <p> </p>
Object detection annotations on images from the Rijksmuseum
<p>A dataset containing annotations made on images of cultural heritage digital objects from the Rijksmuseum . The annotations resulted from the application of automatic object detection techniques. This dataset was used for the evaluation of the object detection model developed by the project Saint George on a Bike. The service achieved a precision of 79.4% and a recall of 65.7% in this dataset. The dataset contains:</p> <ul> <li>Object detection annotations (SgoaB-Rijksmuseum-objectDetection.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. It contains 792 annotations on 315 images.</li> <li>Human-validated object detection annotations (SgoaB-Rijksmuseum-objectDetection-validatedSubset.zip): A ZIP archive containing the enrichments (as annotations) created by the SGoaB project. This data dump includes only the subset of the annotations that were considered correct after human validation. It contains 506 annotations on 283 images.</li> </ul>
Lithium Pollution of White Dwarfs and Other Secrets of MORDOR (CSV File of MORDOR Survey Objects)
<p>CSV file containing the Gaia DR2 data for the MORDOR Survey from Benjamin C. Kaiser's Ph.D. Dissertation. It also contains the spectral types and SED types that were identified.</p> <p>If you use this data please cite my dissertation, which should be accessible via the UNC Chapel Hill Library in some way. You should probably also cite Gaia DR2 if you use anything other than my spectral types pretty much because all the rest of the data is from Gaia DR2.</p>
Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms
<p>The question of whether perceptual expertise is mediated by general-expert or domain-specific processing mechanisms has been debated for decades. Because humans are experts in face recognition, face-like neural and cognitive effects for objects of expertise were considered to support for the general-expertise hypothesis. Conversely, stronger effects for faces than objects of expertise were considered to support the domain-specific hypothesis. However, the effects of domain, experience, and level of categorization, are confounded in human studies, which may lead to erroneous inferences. To overcome these limitations, we used computational models of perceptual expertise and tested different domains (objects, faces, birds) and levels of categorization (basic, sub-ordinate, individual) in isolation, matched for amount of experience. Like humans, the models generated a larger inversion effect for faces than for objects. Importantly, a face-like inversion effect was found for individual-based categorization of non-faces (birds) but only in a network specialized for that domain. Thus, contrary to prevalent assumptions, face-like effects in objects of expertise may originate from domain-specific rather than domain-general processing mechanisms. More generally, we show how deep learning algorithms can be used to isolate the effects of factors that are inherently confounded in the natural environment of biological organisms.</p>
Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks
<p>We provide a multi-sensor dataset containing RGB-D and motion tracking data from sequential human-to-human object handovers. We recorded 12 pairs of participants executing shelving and un-shelving tasks involving 30 object handovers, resulting in 1440 handovers. Each recording consists of the position and orientation trajectories of 13 upper-body bones of the giver and the receiver and position trajectories of the 27 markers placed on their upper bodies, all recorded at 120Hz. The recordings also include two RGB-D data streams at 30Hz. We also provide four anthropometric measurements of the participants: height, waistline height, arm span, and weight. The dataset is valuable for investigating the body movements, grasps, and coordination strategies utilized by humans while performing tasks such as shelving which involve multiple sequential object handovers. Additionally, the dataset can be used to teach robots perform tasks involving object handovers with people, as well as self-handovers to correct grasps.<br><br>The details about the dataset collection procedure are available in the article:<br>Kshirsagar, A., Fortuna, R., Xie, Z., & Hoffman, G. (2025). Descriptor: Multi-sensor Dataset of Multiple Sequential Human-to-Human Object Handovers in Shelving and Un-shelving Tasks (MH2HO). IEEE Data Descriptions. (http://dx.doi.org/10.1109/IEEEDATA.2025.3580058)</p>
Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks (Video)
<p>Supplementary video for the paper "Reactive Correction of Object Placement Errors for Robotic Arrangement Tasks".</p>
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.