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12 results for “Video database”
CVD2014 - A database for evaluating no-reference video quality assessment algorithms
<p>The CVD video database is developed to provide an useful tool for researchers in the validation and developing processes of no-reference (NR) objective video quality assessment (VQA) algorithms. It consists of 234 videos from five different scenes captured by 78 different cameras (mobile phones, compact camera, video camera, SLR). The subjective experiments are conducted following the Single-Stimulus (SS) procedure to collect ratings of video quality.</p> <p><strong>Setup</strong></p> <p>We implement our experiments according to the Single Stimulus methodology using VQone MATLAB toolboxon high quality monitors (Eizo ColorEdge CG241W) with 1920x1200 pixel resolution in a dark room (ambient light < 20 lux). Video stimuli were displayed at their original size of VGA (640 x 480) or HD (1280 x 720). The subjects viewing distance (80 cm) was controlled by a string hanging from a ceiling and they were instructed to keep their head steady next to it. The monitors were calibrated to according to sRGB (target values were: 6500 K, 80 lux, and gamma 2.2) using EyeOne Pro calibrator (X-rite co.). The laboratory setup is showed in the figure below.</p> <p><strong>Subjects</strong></p> <p>Subjects (n = 30, 30, 28, 33, 30, 32 and 27 for Tests 1 - 7 respectively) were naïve in a sense that they did not study or work with image quality or related fields. They were recruited through student mailing lists consisting mainly humanities and behavioral science students. Subjects’ vision was controlled for the near visual acuity, near contrast vision (near F.A.C.T.) and color vision (Farnsworth D15) before the participation. They received movie tickets as a reward.</p> <p><strong>Procedure</strong></p> <p>Subjects evaluated one video sample at a time and all video samples of one scene were presented in a row. The order of video samples and scenes was randomized. Subjects had the option to view video samples again as many times as they wanted.</p> <p><strong>Data</strong></p> <p>The results are processed and reported in the form of Mean Opinion Score (MOS) for the tested video samples. In addition, we provide the whole raw data from the subjective experiments instead of just pre-calculated mean opinion scores from each video sample. This allows further analyses to be made by those who wish to use this database and gives them better opportunity to utilize the data to its full potential.</p> <p>Realignment study (test 7) contains the data from the additional study in which the mappings from the test and scene specific quality scales (test 1-6) to the global quality scale were formed. The global scale is valuable when studying and developing VQA algorithms. With the global scale, all of the samples (234 video samples in the case of the CVD2014) are in the same scale, and the performance analysis for algorithms can be conducted with a high number of samples.</p> <p><strong>If you use this database in your research, we kindly ask that you follow The Copyright notice below and cite the following paper:</strong></p> <p> </p> <p>M. Nuutinen, T. Virtanen, M. Vaahteranoksa, T. Vuori, P. Oittinen and J. Häkkinen, "CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms," in <em>IEEE Transactions on Image Processing</em>, vol. 25, no. 7, pp. 3073-3086, July 2016. doi: 10.1109/TIP.2016.2562513</p> <p> </p> <p> </p> <p>-----------COPYRIGHT NOTICE STARTS WITH THIS LINE------------</p> <p>Copyright (c) 2014 The University of Helsinki<br> All rights reserved.</p> <p>Permission is hereby granted, without written agreement and without license or royalty fees, to use, copy, modify, and distribute this database (the videos, the images, the results and the source files) and its documentation for any purpose, provided that the copyright notice in its entirely appear in all copies of this database, and the original source of this database,Visual Cognition research group (www.helsinki.fi/psychology/groups/visualcognition/index.htm) and the Institute of Behavioral Science (www.helsinki.fi/ibs/index.html) at the University of Helsinki (www.helsinki.fi/university/), is acknowledged in any publication that reports research using this database. Individual videos and images may not be used outside the scope of this database (e.g. in marketing purposes) without prior permission.</p> <p>The database and our paper are to be cited in the bibliography as: M. Nuutinen, T. Virtanen, M. Vaahteranoksa, T. Vuori, P. Oittinen and J. Häkkinen, "CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms," in <em>IEEE Transactions on Image Processing</em>, vol. 25, no. 7, pp. 3073-3086, July 2016.<br> doi: 10.1109/TIP.2016.2562513</p> <p>-----------------------------------------------------------------------------</p> <p>LIMITATION OF LIABILITY</p> <p>UNIVERSITY OF HELSINKI SHALL IN NO CASE BE LIABLE IN CONTRACT, TORT OR OTHERWISE FOR ANY LOSS OF REVENUE, PROFIT, BUSINESS OR GOODWILL OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL OR PUNITIVE COST, DAMAGES OR EXPENSE OF ANY KIND HOWEVER CAUSED OR HOWEVER ARISING UNDER OR IN CONNECTION WITH THE USE OF THIS DATABASE.</p> <p>THE UNIVERSITY OF HELSINKI SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE DATABASE PROVIDED HEREUNDER IS ON AN "AS IS" BASIS, AND THE UNIVERSITY OF HELSINKI HAS NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.</p> <p>THIS AGREEMENT SHALL BE CONSTRUED AND INTERPRETED IN ACCORDANCE WITH THE LAWS OF FINLAND, EXCLUDING ITS RULES FOR CHOICE OF LAW.</p> <p>-----------COPYRIGHT NOTICE ENDS WITH THIS LINE------------</p>
DATABASE OF THE DIGITAL ELEVATION MODELS OF THE SKEIÐARÁRSANDUR KETTLE-HOLES (S ICELAND), JUNE 2022 - Part II. VIDEO
<p>The database contains 83 video files in .MOV format, shot by a digital camera at 23.98 frames per second. The average length of videos is 100–600 seconds. They are documentation of fieldwork carried out in June 2022, aimed at preparing the footage to generate high-resolution digital elevation models (Part I) using the 'Structure from Motion' technique. The study covered kettle-holes of the glacial flood origin located at Skeiðarársandur in S Iceland.</p>
ATLAS Database — Videos
<p>We need affect-based stimuli specifically conceived to investigate architectural spaces. The RESONANCES project crafted ATLAS, a dATabase of visuaL Atmospheric Stimuli. It collects a series of spatial patterns born from a systematic selection of generators of atmosphere. Generators of atmosphere are architectural features designed to afford atmospheric effects (such as lights, colors, materials, and proportions). ATLAS is an open-access tool that supports researchers interested in studying emotional reactions to architectural features by providing reliable, standardized, and reproducible stimuli. In this dataset, ATLAS stimuli are presented as videos.</p>
Interlinking dataset of the video game databases Mobygames and MediaArt Database
<p>The goal of the DFG (German Research Foundation) funded project diggr (Databased Infrastructure for Global Games Culture Research) is the integration of information from various heterogeneous, fragmentary online resources of the video game domain with the aim of creating robust research datasets.</p> <p>This dataset provides automatically generated links between the video game databases Mobygames (http://www.mobygames.com) and Media Art DB (https://mediaarts-db.bunka.go.jp/gm/) and additional metadata for each game entry.</p> <p>A full description of the dataset is provided in <em>Dataset Description.md.</em></p>
FIRESENSE database of videos for flame and smoke detection
<p>The FIRESENSE database, developed within the FP7-ENV-244088<br> ''FIRESENSE - Fire Detection and Management through a Multi-Sensor Network for<br> the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme<br> Weather" project contains videos for testing flame and smoke detection algorithms.</p> <p>Specifically:</p> <p>a) for flame detection 11 positive and 16 negative videos are provided, while</p> <p>b) for smoke detection, 13 positive and 9 negative videos are provided.</p> <p>Results using this database are presented in many papers, including:</p> <ol> <li><strong>K. Dimitropoulos, P. Barmpoutis, N. Grammalidis, </strong>"<em>Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection</em>", IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Vol. 25, No. 2, February 2015, pp. 339-351.</li> </ol> <p> </p> <p> </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
"Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar video recording
<p>Video recording of the "Presenting Euroqcharm project outcomes to foster global database synchronization" Zoom Webinar</p>
ENF Moving Video Database
<p>Moving Video Database. MP4 files correspond to video files. WAV files are the voltage signals obtained from the mains. The recording time of the MP4 files and they WAV reference is very similar.</p> <p>Three types of videos are considered: MOV1 are static videos, MOV2 are videos with movement but most of the frames are static, while in MOV3 videos almost all frames contain movement.</p> <p>Any doubt contact sfmenduina@gts.uvigo.es</p>
Database for "Automatic Detection of Instream Large Wood in Videos Using Deep Learning"
<p>The database contains 21 datasets. Each dataset consists of a folder ('jpgs') with images and a folder ('txts') with detections according to the YOLO standard.</p>
Multi-Dimensional Simulation of the Volumetric Database by Video - Mapping Technology of Sea surface temperature remotley sensed (Algerian basin) ( SST : 2003 - 2007 - 2015 - 2019 ) (L4, MUR,GHRSST)
<p><span>Marine data, which are volumetric data, contain a variety of information about a water body (e.g., seawater temperature, salinity, density, and current) (Feng Zhang et all.,2019),due to its large scale, random variation and multi-resolution in nature, are hard to be visualized and analyzed (Jerry, 2001; Claes, 2004). Moreover, people can not observe the internal characteristics and phenomena of the ocean directly and comprehensively. ‘Digital Ocean’, which emerged after ‘Digital Earth’, focuses on analyzing ocean phenomena and serves for ocean applications. ‘Digital Earth’ is an information system including massive, multi-resolution, multi-temporal marine databases and analysis algorithms (Shi and Lei, 2011). Nowadays, constructing an ocean model and visualizing volumetric data have become some of the most important and critical research topics of ‘Digital Ocean’. The study of marine volumetric data visualization can be valuable in many other research areas (Coelho et al., 2004).</span></p> <p><span>Volume visualization, one of the most important fields of scientific visualization, is the process of generating meaningful and visual information on a two-dimensional image plane from three dimensional datasets. It has been increasingly important in geographical information systems to improve the ocean modeling (Gonzato and Saec, 2000; Djurcilov et al., 2002)</span> </p> <p><strong><span>REF :</span></strong></p> <p><span>Feng Zhang, Ruichen Mao, Zhenhong Du, Renyi Liu,Spatial and temporal processes visualization for marine environmental data using particle system,Computers & Geosciences, Volume 127,2019,Pages 53-64,ISSN 0098-3004,https://doi.org/10.1016/j.cageo.2019.02.012.</span></p> <p><span>Jerry, T., 2001. Simulating ocean water. ACM SIGGRAPH (Special Interest Group on Computer Graphics) 2001 Course Notes, Los Angeles, <a href="http://home1.get.net/tssndrf/"><span>http://home1.get.net/tssndrf/</span></a>.</span></p> <p><span>Claes, J., 2004. Real time water rendering. Master thesis. Department of Computer Science, Lund University</span></p> <p><span>Shi, S. X., and Lei, B., 2011. Theory and Practice on China Digital Ocean. Ocean Press, Beijing, 80-100.</span></p> <p><span><span>Coelho, A., Nascimento, M., Bentes, C., de Castro, M. C. S.,and Farias, R., 2004. Parallel volume rendering for ocean visualization in a cluster of PCS. In: <em>Proceeding of VI Brazilian</em> <em>Symposium on Geoinformatics</em>, Campos do Jordão, São Paulo, Brazil, 22- 24.</span></span></p> <p><span>Gonzato, J. C., and Saec, B. L., 2000. On modeling and rendering ocean scenes. <em>Journal of Visualisation and Computer</em> <em>Animation</em>, <strong>11 </strong>(1): 27-37.</span></p> <p><span>Djurcilov, S., Kim, K., Lermusiaux, P. F. J., and Pang, A., 2002.Visualizing scalar volumetric data with uncertainty. <em>Computers</em> <em>and Graphics</em>, <strong>2 </strong>(26): 239-248.</span></p>
ENF Moving Video Database I
<p>A moving video database to contrast the performance of different ENF extraction methods from digital videos.</p>
SWAN-DF database of audio-video deepfakes
<p><strong>Description</strong></p> <p>SWAN-DF: the first high fidelity publicly available dataset of realistic audio-visual deepfakes, where both faces and voices appear and sound like the target person. The SWAN-DF dataset is based on the public <a href="https://www.idiap.ch/dataset/swan">SWAN database</a> of real videos recorded in HD on iPhone and iPad Pro (in year 2019). For 30 pairs of manually selected people from SWAN, we swapped faces and voices using several autoencoder-based face swapping models and using several blending techniques from the well-known open source repo <a href="https://github.com/iperov/DeepFaceLab">DeepFaceLab</a> and voice conversion (or voice cloning) methods, including zero-shot <a href="https://github.com/Edresson/YourTTS">YourTTS</a>, <a href="https://github.com/huawei-noah/Speech-Backbones/tree/main/DiffVC">DiffVC</a>, <a href="https://github.com/tinkoff-ai/hifi_vc">HiFiVC</a>, and several models from <a href="https://github.com/OlaWod/FreeVC">FreeVC</a>.</p> <p>For each model and each blending technique, there are 960 video deepfakes. We used three types of models of the following resolutions: 160x160, 256x256, and 320x320 pixels. We took one pre-trained model corresponding for each resolution, and tuned it for each of the 30 pairs (both ways) of subjects for 50K iterations. Then, when generating deepfake videos for each pair of subjects, we used one of the tuned models and a way to blend the generated image back into the original frame, which we call blending technique. SWAN-DF dataset contains 25 different combinations of models and blending, which means the total number of deepfake videos is 960*25=24000.</p> <p>We generated speech deepfakes using four voice conversion methods: YourTTS, HiFiVC, DiffVC, and FreeVC. We did not use text to speech methods for our video deepfakes, since the speech they produce is not synchronized with the lip movements in the video. For YourTTS, HiFiVC, and DiffVC methods, we used the pretrained models provided by the authors. HiFiVC was pretrained on VCTK, DiffVC on LibriTTS, and YourTTS on both VCTK and LibriTTS datasets. For FreeVC, we generated audio deepfakes for several variants: using the provided pretrained models (for 16Hz with and without pretrained speaker encoder and for 24Hz with pretrained speaker encoder) as is and by tuning 16Hz model either from scratch or starting from the pretrained version for different number of iterations on the mixture of VCTK and SWAN data. In total, SWAN-DF contains 12 different variations of audio deepfakes: one for each of YourTTS, HiFiVC, and DiffVC and 9 variants of FreeVC.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>If you use this database, please cite the following publication:</p> <p>Pavel Korshunov, Haolin Chen, Philip N. Garner, and Sébastien Marcel, "Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes", IEEE International Joint Conference on Biometrics (IJCB), September 2023.<br> <a href="https://publications.idiap.ch/publications/show/5092">https://publications.idiap.ch/publications/show/5092</a></p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.