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Dataset results
156 results for “Video dataset”
A dataset for Customer Churn Prediction for Video Websites Incorporating Behavioral Sequence Features
<p>In order to study the issue of network customer churn, the iQiyi customer dataset was collected. Behavioral sequence features were extracted from it to build a deep learning model and experiments were conducted.Here, we provide the corresponding raw dataset, including all the data we used.</p>
Sonification of Chemotactic Waves of Bacteria - Video Dataset
<p>Sonifications of videos of fluorescent <em>E. coli</em> bacteria migrating towards or away from an agar interface with a chemical of interest. </p> <p>Collated for The 28th International Conference on Auditory Display (ICAD 2023) June 26 – July 1 2023, Norrköping, Sweden</p>
RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation
<p>We introduce the first video-based <strong>retinal vessel dataset (RVD)</strong>, a collection of 635 smartphone-based videos with detailed vessel annotation. All captured videos have a frame rate of 25 frames per second, with the duration varying between 2 to 30 seconds. The total number of frames in our dataset is over 130,000. These videos are recorded from four clinics, including patients from 50 to 75 years old. More specifically, 264 males and 151 females are included in the collection process.</p> <p>The annotations provided in our dataset span two dimensions: spatial and temporal. In the spatial dimension, we offer three distinct levels of annotations: binary vessel masks, general vein-artery masks, and fine-grained vein-artery masks. Each kind of annotation is tailored to specific clinical purposes. In the temporal dimension, we focus on the optic disk regions of videos where the retinal vessel fluctuation normally occurs. We select and annotate frames with the maximal and minimal pulse widths as well as label the existence of spontaneous retinal venous pulsations (SVP).</p> <p>More detailed information can also be found on our website: https://uq-cvlab.github.io/Retinal-Video-Dataset/</p> <p> </p> <p>If you find our RVD dataset is useful in your research, please consider cite:</p> <pre><code>@article{MD2023RVD, title={RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation}, author={MD WAHIDUZZAMAN KHAN, Hongwei Sheng, Hu Zhang, Heming Du, Sen Wang, Minas Theodore Coroneo, Farshid Hajati, Sahar Shariflou, Michael Kalloniatis, Jack Phu, Ashish Agar, Zi Huang, Mojtaba Golzan, Xin Yu}, journal={arXiv preprint arXiv:2307.06577}, year={2023} } </code></pre> <p> </p> <p> </p> <p> </p>
DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and Small Text
<p>Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community.However, most existing algorithms and benchmarks focus on common text cases~(\eg normal size, density) and single scenario, while ignoring extreme video text challenges, \ie{} dense and small text in various scenarios. In this paper, we establish a video text reading benchmark, named DSText V2, which focuses on \textbf{D}ense and \textbf{S}mall text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotters to track and read. 2) High-proportioned small texts, coupled with the blurriness and distortion in the video, will bring further challenges. 3) Various new scenarios, \eg{} `Game', `Sports', etc. The proposed DSText V2 includes 140 video clips from 7 open scenarios, supporting three tasks, \ie{} video text detection (Task 1), video text tracking (Task 2), and end-to-end video text spotting (Task 3). In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols, and the results summaries. Most importantly, a thorough investigation and analysis targeting three unique challenges derived from our dataset are provided, aiming to provide new insights. Moreover, we hope the benchmark will promise video text research in the community. DSText v2 is built upon DSText v1, which was previously introduced to organize the ICDAR 2023 competition for dense and small video text.</p>
Dataset for Project T2EDK-04581: DFVA (Deep Football Video Analytics)
<p>This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call "RESEARCH-CREATE-INNOVATE", project DFVA (Deep Football Video Analytics, project code: T2EDK-04581)</p>
Dataset and trained models for video denoising in fluorescence guided surgery
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A data archive including processed spiking data, raw EMG datasets, and video data during locomotion behavior from six mice
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Kinect Dataset (Raw video) (Part 1)
<p>Contains the raw video of the long and short captures.</p><p>Data structure:<br>long_capture/calibration.json</p><p>long_capture/data.jsonl</p><p>long_capture/data.mkv</p><p>long_capture/data2.mkv</p><p>long_capture/vio_config.yaml</p><p>short_capture/calibration.json</p><p>short_capture/data.jsonl</p><p>short_capture/data.mkv</p><p>short_capture/data2.mkv</p><p>short_capture/vio_config.yaml</p>
Kinect Dataset (Raw video) (Part 2)
<p>Contains the raw video of the long and short captures.</p><p>Data structure:<br>long_capture/calibration.json</p><p>long_capture/data.jsonl</p><p>long_capture/data.mkv</p><p>long_capture/data2.mkv</p><p>long_capture/vio_config.yaml</p><p>short_capture/calibration.json</p><p>short_capture/data.jsonl</p><p>short_capture/data.mkv</p><p>short_capture/data2.mkv</p><p>short_capture/vio_config.yaml</p>
textual item similarty (video games and wines datasets)
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Thermal Video Dataset of Hand Gestures
<p>Our dataset is captured with the FLIR Lepton 2.5 thermal camera, which comes with a radiometric shutter and a focal length of 80X60 pixels. The camera's spectral range is 8 µm to 14 µm, and it has a 63.5 degrees of diagonal field of view and a 50 degrees of nominal horizontal field of view. The camera uses a progressive scan array format of 80X60 pixels.</p> <p>The dataset contains 990 sequences of thermal videos each made up of 15/10/5 frames. It's built up of 9 hand gestures, each with 10 classes within. We had eleven individuals do the gestures to ensure a variety of hand sizes, shapes, and temperatures. </p>
Video demonstration of the use of the neuroglancer interface to explore the H01 dataset.
<p><strong>Video demonstration of the use of the neuroglancer interface to explore the H01 dataset. </strong>Methods demonstrated include 3D navigation within neuroglancer, controlling neuroglancer layers, selecting segments, viewing synapses associated with segments, and filtering for segments by keywords.</p>
Dataset of Outcome of video laryngoscopy versus direct laryngoscopy in ED
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IMDb Popularity Video Games Dataset
<p>This dataset, extracted on April 15, 2024, is presented in JSON format and contains detailed information about video games obtained by <em>web scraping </em>the top 100 of IMDb's popularity ranking on April 16, 2024. </p> <p>The dataset consists of 92 tuples and 15 fields, detailing various aspects of each video game. Included fields cover the game's title, its position in the popularity ranking, release date, countries of origin, official website URLs, primary languages, genres, production companies, main cast, nominations and awards received, parental guidance indicating content level, weighted average rating, user voting distribution, and a link to the corresponding IMDb page.</p>
Dataset for Effects of a Video-Supported Cawthorne-Cooksey Exercise Program
<p>This dataset includes raw and processed data from a randomized pilot study investigating the effects of a video-supported Cawthorne-Cooksey exercise program in older adults with balance deficits and dizziness.</p>
Heavyweight Faceswap Video Dataset
<p>This dataset consists of the 300 original videos modified using deepfake manipulation methods. The 300 videos are split into zip files of 30 videos each for your convenience. Deepfake manipulation is a learning-based faceswap approach that uses poisson imaging to merge a manipulated face with another image. </p>
Dataset: Gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks
<p>Dataset of the <strong>Gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks</strong> article.</p> <p>Further information is available in the README file.</p>
Dataset: Hand-pose estimation videos
<p>These digital artifacts provide a set of videos used as an input dataset for a qualitative evaluation of the pieces of software developed in the context of the Gesture Tracking for Low-End Virtual Reality Systems (GT4LEVRS) project. Each video shows a user making different sorts of gestures: static gestures, slow-moving gestures, and fast-moving gestures. The following files is provided</p> <ol> <li>"2022-05-25 18-57-07.mp4" shows a demonstration of the prototype</li> <li>"Cenario estatico-20220624T180204Z-001.zip" provides five set of videos of static gestures as follows: <ol> <li>Affirmative/OK signal (Folder: Afirmativo)</li> <li>Claw (Folder: Garra)</li> <li>Hang loose gesture (Folder: Hang loose)</li> <li>Opened Hand (Folder: Palma)</li> <li>Positive gesture (Folder: Positivo)</li> </ol> </li> <li>"cenario lento-20220624T180237Z-001.zip" provides five set of videos of low-moving gestures as follows: <ol> <li>Touching hands (Folder: maos encostando)</li> <li>Moving little finger (Folder: mindinho)</li> <li>Holding an object and moving it (Folder: objeto)</li> <li>Moving peace signal (Folder: paz)</li> <li>Moving fists (Folder: punho)</li> </ol> </li> <li>"Cenario rapido-20220624T180245Z-001.zip" provides five set of videos of fast-moving gestures as follows: <ol> <li>Goodbye (Folder: acenar)</li> <li>Moving indication (Folder: apontar)</li> <li>Throwing an object (Folder: arremessar objeto)</li> <li>Crossing hands (Folder: cruzar maos)</li> <li>Clapping hands (Folder: palmas)</li> </ol> </li> </ol> <p>Source code and other design artifacts can be found on Github: <a href="https://github.com/lesc-utfpr/gt4levrs">https://github.com/lesc-utfpr/gt4levrs</a></p> <p>This dataset was created in the context of a bachelor's final thesis in the Computer Engineering course of Federal University of Technology - Paraná (UTFPR), campus Curitiba, by Lucas Kuttner Amin and Rafael Hideo Toyomoto supervised by Prof. Marco Wehrmeister.</p>
Everyday Conversation Videos Dataset
<p>This dataset contains short (all about 1 minute long) videos (at least 1080p) from four different actors (A1-A4) acting out a set of eight scenarios (S1-S8). The scenarios cover a range of speech styles in professional and personal contexts. Each simulates a conversational situation and are filmed from the perspective of he listener. The monologues for these scenarios were written by an experienced script writer. The script writer and all actors were recruited on fiverr.</p>
Everyday Augmented Conversation Videos Dataset
<p>This dataset builds on the <a href="https://zenodo.org/deposit/6901792">Everyday Conversation Videos Dataset</a> and includes augmented variants of those videos. The 160 videos in this dataset all depict about a minute long everyday conversations. For these, four different actors (A1-A4) acted out a set of eight scenarios (S1-S8) Here, the videos have been cropped, trimmed, and standardized to 720p at 30fps. We then added Snapchat filters to the videos by routing them through Snap Camera. Each video is then available in one of five filter conditions, where:</p> <ul> <li>F0: No filter added</li> <li>F01: Beautification filter</li> <li>F02: Subtle beautification filter</li> <li>F03: Face and body tattoo filter</li> <li>F1-F8: Scenario-specific filters</li> </ul>
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.