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Dataset results
26 results for “person detection”
PEDRo: an Event-based Dataset for Person Detection in Robotics
<p>This event-based dataset is specifically designed for person detection in robotics applications. </p>
Dataset for Website Personality Detection
<p>This dataset supports research on identifying the personality of websites. It contains data from 3,000 websites, covering five distinct website categories, and provides quantitative elements extracted from these sites. Additionally, the dataset includes information about the selected website categories, as well as details on website personality "Facets" and "Items." The dataset is accompanied by survey results related to the research.</p>
Small Object Aerial Person Detection Dataset
<p><strong>Small Object Aerial Person Detection Dataset:</strong></p> <p>The aerial dataset publication comprises a collection of frames captured from unmanned aerial vehicles (UAVs) during flights over the University of Cyprus campus and Civil Defense exercises. The dataset is primarily intended for people detection, with a focus on detecting small objects due to the top-view perspective of the images. The dataset includes annotations generated in popular formats such as YOLO, COCO, and VOC, making it highly versatile and accessible for a wide range of applications. Overall, this aerial dataset publication represents a valuable resource for researchers and practitioners working in the field of computer vision and machine learning, particularly those focused on people detection and related applications.</p> <p> </p> <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>People</td> </tr> <tr> <td>Training</td> <td>2092</td> <td>40687</td> </tr> <tr> <td>Validation</td> <td>523</td> <td>10589</td> </tr> <tr> <td>Testing</td> <td>521</td> <td>10432</td> </tr> </tbody> </table> <p> </p> <p>It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).</p>
Person Detection on Construction Sites
<p>All information can be found here:</p> <p><a href="https://github.com/aidresden/person_detection_construction_sites" target="_blank" rel="noopener">https://github.com/aidresden/person_detection_construction_sites</a></p> <p>The development of this data set was funded by the Federal Ministry of Labour and Social Affairs (BMAS) and the Federal Institute for Occupational Safety and Health (BAuA) under the administrative agreement ‘Artificial Intelligence in a Safe and Healthy Working Environment’.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 11. Activated Neuro-Symbols for Detecting that a Person walks around in the Room
<p>With the example of Figure 11, also the function of feedback connections can be explained.<br> According to the existing feedforward connections, the neuro-symbol “object stands” would be<br> activated together with the neuro-symbol “object moves” whenever the neuro-symbols “motion”<br> and “object moves” are active, because it is activated by a subset of the neuro-symbols that activate<br> the neuro-symbol “object moves”. This activation would however be undesired in this concrete<br> case. For this reason, an inhibitory feedback connection exists from the neuro-symbol “object<br> moves” to the neuro-symbol “object stands” that inhibits the activation of the neuro-symbol “object<br> stands”.</p>
Supplementary data to: Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma
<p>Supplemental Data for the patient described in the manuscript: "Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma". Summary of somatic variant calls from DNA whole exome, gene FPKM from RNA sequencing, and neoantigen predictions for high-affinity (ic<sub>50</sub> <500 nM) candidates.</p>
The signal detection problem of aposematic prey revisited: integrating prior social and personal experience
<p>Data collected during three separate experiments using the "novel world" (Alatalo & Mappes, Nature 1996) approach to test how social information changes predator discrimination of novel aposematic prey from a cryptic palatable alternative. Experiments were conducted with great tits (<em>Parus major</em>), captured from the wild and released afterwards, at the University of Jyväskylä Research Station, Konnevesi, Finland (62.6° N, 26.3° E) during three winters (2013-2014, 2016-2017, 2017-2018). Social information was provided by video playback of a demonstrator (adult male) showing an aversive behavioural response to a novel prey signal before observers (juveniles, adults, males, females) searched for prey signals against a background in either an aviary or in a "miniature novel world" in an experimental holding box.</p>
Safety, Tolerability and Efficacy of Molnupiravir (EIDD-2801) to Eliminate Infectious Virus Detection in Persons With COVID-19
ClinicalTrials.gov study NCT04405570. IPD Sharing: NO. Countries: 1. Publications: 4.
The signal detection problem of aposematic prey revisited: integrating prior social and personal experience
Open the record for dataset details and reuse information.
SDT Dataset | Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification
<p>The Synthetic Depth & Thermal (SDT) dataset consists of 40k synthetic and 8k real depth and thermal stereo images, depicting human behavior in indoor environments. Included samples show uniquely posed lying, sitting, and standing persons within four different room types (living room, bedroom, bathroom, and kitchen), recorded from an elevated position. Furthermore, a fourth control class with empty rooms is provided as well. Both parts of SDT are balanced sets of these four classes and room types. The synthetic part of the dataset is intended to be used as training (and validation) data for uni-/multi-modal pose classification or person detection models, while the real part can be used to assess the generalization performance. To facilitate supervised training, pose labels and person bounding boxes are provided for all images. The real images in the dataset were captured by a multi-modal stereo camera system, consisting of an Orbbec Astra depth camera and a FLIR Lepton 3.5 thermal camera, while synthetic images, which share the image characteristics of these cameras, were acquired through 3D rendering of virtual scenes within Blender and subsequent introduction of camera-specific noise.</p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper [1].</p> <p>[1] C. Pramerdorfer, J. Strohmayer and M. Kampel, "Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification," <em>2020 IEEE International Conference on Image Processing (ICIP)</em>, Abu Dhabi, United Arab Emirates, 2020, pp. 1611-1615, doi: 10.1109/ICIP40778.2020.9191284.</p> <p>BibTeX citation:</p> <pre>@INPROCEEDINGS{9191284, author={Pramerdorfer, C. and Strohmayer, J. and Kampel, M.}, booktitle={2020 IEEE International Conference on Image Processing (ICIP)}, title={Sdt: A Synthetic Multi-Modal Dataset For Person Detection And Pose Classification}, year={2020}, volume={}, number={}, pages={1611-1615}, doi={10.1109/ICIP40778.2020.9191284}}</pre>
Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
Person detection using UWB and Monocular camera (With LiDAR ground-truth)
<p>This dataset was record using the ROS2-foxy framework and can be utilized with:</p> <pre><code>ros2 bag play square_test_with_gt</code></pre> <table> <tbody> <tr> <td>Name of ROS2 topic</td> <td>Type of ROS2 topic</td> <td>Information</td> </tr> <tr> <td>/Detections</td> <td>vision_msgs/msg/Detection2DArray</td> <td>This topic includes person detections from the monocular camera that is performing the Deep Learning object detection</td> </tr> <tr> <td>/GT_POINT</td> <td>geometry_msgs/msg/PointStamped</td> <td>Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes</td> </tr> <tr> <td>/distance_data_array</td> <td>itrci_hardware/msg/RadioRangeDataArray</td> <td>This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)</td> </tr> <tr> <td>/tf</td> <td>tf2_msgs/msg/TFMessage</td> <td>base_link and odom tf (robot is static)</td> </tr> <tr> <td>/tf_static</td> <td>tf2_msgs/msg/TFMessage</td> <td>Contains tf information of LiDAR cameras and anchors relative to the robot base_link</td> </tr> </tbody> </table>
Person detection using UWB and Monocular camera (With LiDAR ground-truth) 0.7m/s
<p>This dataset was record using the ROS2-foxy framework and can be utilized with:</p> <pre><code>ros2 bag play square_test_with_gt_2 </code></pre> <table> <tbody> <tr> <td>Name of ROS2 topic</td> <td>Type of ROS2 topic</td> <td>Information</td> </tr> <tr> <td>/Detections</td> <td>vision_msgs/msg/Detection2DArray</td> <td>This topic includes person detections from the monocular camera that is performing the Deep Learning object detection</td> </tr> <tr> <td>/GT_POINT</td> <td>geometry_msgs/msg/PointStamped</td> <td>Contains the PointStamped message obtained from the LiDAR person detection for ground truth purposes</td> </tr> <tr> <td>/distance_data_array</td> <td>itrci_hardware/msg/RadioRangeDataArray</td> <td>This topic has person detections from the 3 UWB Anchors relative to the person TAG (Note that is in custom ros2 message itrci_hardware)</td> </tr> <tr> <td>/tf</td> <td>tf2_msgs/msg/TFMessage</td> <td>base_link and odom tf (robot is static)</td> </tr> <tr> <td>/tf_static</td> <td>tf2_msgs/msg/TFMessage</td> <td>Contains tf information of LiDAR cameras and anchors relative to the robot base_link</td> </tr> </tbody> </table>
Coronary Obstruction Detection by Molecular Personalized Gene Expression (Corus CAD or ASGES)
ClinicalTrials.gov study NCT01117506. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Sentinel™ Trial: A Prospective Study Evaluating the Performance and Clinical Benefit of Personalized ctDNA-based MRD Testing to Detect Recurrence and Monitor Treatment Response in Patients With Solid
ClinicalTrials.gov study NCT05082701. IPD Sharing: NO. Countries: 1. Publications: 7.
Enhancing Linkage to HIV Care for Newly Detected HIV-positive Persons in Ukraine
ClinicalTrials.gov study NCT02338024. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The PREPAIRD Study: Personalized Surveillance for Early Detection of Pancreatic Cancer in High Risk Individuals
ClinicalTrials.gov study NCT05740111. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Personalized Detection of ctDNA for Patients With HER2-Positive Metastatic Breast Cancer Who Achieved Durable Response by Anti-HER2 Treatment (HER2 CR)
ClinicalTrials.gov study NCT06663787. IPD Sharing: UNDECIDED. Countries: 2. Publications: 0.
Universal Endometrial Cancer DNA Sequencing for Detection of Lynch Syndrome and Personalized Care
ClinicalTrials.gov study NCT03460483. IPD Sharing: NO. Countries: 1. Publications: 0.
Detection of Asymptomatic Atrial Fibrillation in Persons of 65 Year of Age or Older
ClinicalTrials.gov study NCT04375241. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
ScienceDex guides
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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.