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Dataset results
7 results for “Video Surveillance”
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
<p>The data comes from common guillemots studied at Stora Karlsö, Sweden between 2019 and 2021. The common guillemots breed at an artificial cliff, and has been filmed continusly from above over three breeding seasons. Using the video material, a YOLOv5 model has been trained to detect adult birds, chicks and eggs. The dataset contains annotations (bounding boxes) used for training the model, the model itself, and outputs from the model (object detections).</p> <p>The data can be used and shared freely.</p>
Common guillemots in the Baltic Sea studied with video surveillance and object detection: raw data, annotations, model, and model outputs
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Toulouse Campus Surveillance Dataset: scenarios, soundtracks, synchronized videos with overlapping and disjoint views
<p>The Toulouse Campus surveillance Dataset, named ToCaDa, contains two sets of 25 temporally synchronized videos corresponding to two scripted scenarios.<br> <br> With the help of about 50 persons (actors and camera holders), these videos were shot on July 17th 2017 at 9:50 a.m. and 11:04 a.m. respectively.<br> <br> Among the cameras:<br> • 9 were located inside the main building and shot from the windows at different floors. All these cameras are focusing the car park and the path leading to the main entrance of the building with large overlapping fields of view.<br> • 8 were located in front of the building and filmed it with large overlapping fields of view.<br> • 8 cameras were arranged further, scattered around the university campus. Each of their views is disjoint from all the others.<br> <br> About 20 actors were asked to follow two realistic scenarios by performing scripted actions, like driving a car, walking, entering or leaving a building, or holding an item in hand while being filmed.<br> <br> In addition to ordinary actions, some suspicious behaviors are present.</p> <p><strong>Irregularities:</strong></p> <p>Due to the wide variety of devices used during the shooting of the two scenarios, issues were encountered on some cameras, leading to videos where a few seconds are lacking. To ensure temporal synchronization between videos, black frames were added on the missing intervals of time. We list these particular videos and their lacking times below:</p> <p>F1C3: the first 66 seconds are missing.<br> F1C5: the first 2 seconds are missing.<br> F1C8: the first 3 seconds are missing.<br> F1C13: the first 10 seconds are missing.<br> F1C15: the first second is missing.<br> F1C19: the first second is missing.<br> F2C1: the video is accelerated and only lasts a few seconds. We thus did not provide it.<br> F2C6: lacks from 4:01 to 4:12 and from 4:25 to 4:28.<br> F2C16: lack from 5:15 to 5:26.</p> <p>Some videos were recorded with mobile devices whose pixel resolution was lower than 1920 x 1080:</p> <p>F1C3 and F2C3: pixel resolution is 1280 x 720.<br> F1C4 and F2C4: pixel resolution is 640 x 480.<br> F1C15 and F2C15: pixel resolution is 1280 x 720.<br> F1C20 and F2C20: pixel resolution is 1440 x 1080.<br> <br> More detailed information about the position of the cameras can be found on the following link:<br> <a href="http://ubee.enseeiht.fr/dokuwiki/doku.php?id=public:tocada">http://ubee.enseeiht.fr/dokuwiki/doku.php?id=public:tocada</a><br> <br> <strong>Citation</strong><br> T. Malon, G. Roman-Jimenez, P. Guyot, S. Chambon, V. Charvillat, A. Crouzil, A. Péninou, J. Pinquier, F. Sèdes and C. Sénac, Toulouse campus surveillance dataset: scenarios, soundtracks, synchronized videos with overlapping and disjoint views, ACM Multimedia Systems Conference, 2018.</p>
One Health EJP MATRIX video about the One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP)
<p>This video was recorded during the webinar about the <strong>One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP) </strong>as part of the OHEJP MATRIX Webinar Series 2022 <em>Solutions for One Health Surveillance in Europe</em>. The webinar took place on November 10<sup>th</sup> from 14:00 to 15:15 CET and was hosted by the <a href="https://onehealthejp.eu/jip-matrix/">One Health EJP project MATRIX.</a></p> <p>The One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP) is a community resource supporting the adoption of the OH paradigm. The OHS Codex/KIP comprises five high-level “action principles”, which respectively support: i) planning and management; ii) collaboration; iii) knowledge exchange; iv) data interoperability; v) reporting and dissemination. These principles are applicable to any sector-specific or cross sectorial surveillance activity.<br> Under each of these principles, the OHS Codex/KIP provides the users a collection of resources (e.g. tools, technical resources, guidance documents and experiences) that address specific OH-problems in the context of the corresponding principle. As an open community framework, it is continuously updated by and for the community. More information is available <a href="https://oh-surveillance-codex.readthedocs.io/en/latest/">here.</a></p> <p>The OHS Codex/KIP has been initially devolped in <a href="https://onehealthejp.eu/jip-orion/">One Health EJP ORION</a> and was expanded and promoted within <a href="https://onehealthejp.eu/jip-matrix/">One Health EJP MATRIX.</a></p> <p>The MATRIX and ORION projects are part of the One Health European Joint Programme (OHEJP).<br> They received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.</p> <p>Leading MATRIX partner of the OHS Codex/KIP: German Federal Institute for Risk Assessment (BfR). Contact: Matthias Filter (Matthias.Filter@bfr.bund.de)</p> <p><em><strong>Webinar agenda:</strong></em></p> <p><strong>Welcome and general overview of the MATRIX solutions for One Health Surveillance</strong><br> Guido Benedetti, Statens Serum Institut (SSI), Denmark</p> <p><strong>One Health Surveillance CODEX: Knowledge Integration Platform – ORION perspective and MATRIX extension</strong><br> Matthias Filter, German Federal Institute for Risk Assessment (BfR), Germany</p> <p><strong>Live Demo and special features of the One Health Surveillance CODEX: Knowledge Integration Platform</strong><br> Yvonne Mensching, German Federal Institute for Risk Assessment (BfR), Germany</p>
Superiority of Intelligent Video Surveillance + Telealarm Over Telealarm Alone in Elderly People at Risk of Falling
ClinicalTrials.gov study NCT05875038. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity
Open the record for dataset details and reuse information.
TileClipper: Lightweight Selection of Regions of Interest from Videos for Traffic Surveillance
<p>Artifact for USENIX ATC'24: TileClipper: Lightweight Selection of Regions of Interest from Videos for<br>Traffic Surveillance</p>
ScienceDex guides
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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.