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156 results for “Video dataset”
Annotated Video Dataset of Fencing Movements and Corresponding Error Patterns
<p>Video dataset containing 1289 videos and their augmentation of four fencing movements, performed by a variety of fencers. The main actions included are counterattack, lunge, flèche and parry. For each movement samples with typical error patterns are included and annotated. Additionally labels for mulit-labelling are documented. The corresponding paper "Mastering Fencing Techniques with Machine Learning: A Video-Based Classification and Correction System" is published at the 10th IEEE Swiss Conference on Data Science (SDS 2023)</p>
Dataset for surface waves height prediction through the video and image processing
<p>Image-based study of surface waves is a long lasting topic in ocean science and remote sensing. We believe that modern computers and new programming techniques can make a break-through in this area.</p> <p> </p> <p>This dataset provides some video files of surface wind waves of two kinds. First is a video snapshot of a quite large area. Second one is a zoom-in video of a spar-buoy (a stick) located in this field. According to the zoom-in video we may see the actual height of the wave in this particular point. This should be treated as a reliable data and so it can be used to calibrate the brightness field. I.e. the users of this dataset are welcome to train their model to obtain the height of the wave out of its brightness on the zoom-out large-area videos.</p> <p> </p> <p>All video files are readable by a conventional software. Records were taken at mild wind conditions in a gulf (fjord or skerry) of the Ladoga Lake. See "readme.pdf" for the details</p>
Transit Bus Left Side Camera Video Traffic Monitoring Dataset
<p>This dataset contains the raw data used in the research and development study reported in “Automated Traffic Surveillance Using Existing Cameras on Transit Buses”.</p> <p>This dataset consists of 11 video clips, each approximately 20 min. in duration, taken from the driver (left) side read camera of an in-service Ohio State University (OSU) Campus Area Bus Service (CABS) 40-foot transit bus running the West Campus Loop route. One set, consisting of 7 videos, was collected on a sunny day in October 2019. A second set, consisting of 3 videos, was collected during and after periods of rain and heavy rain in March 2022. Each video is accompanied by manually extracted ground truth of vehicles, and some other objects, that are in the roadway and observed by the camera.</p> <p> </p>
Dataset: Assessing Power in Punching Sounds from Mainstream Films and Video Games
<p>Dataset accompanying the article published at ICAD 2023: </p> <p>Assessing Power in Punching Sounds from Mainstream Films and Video Games</p> <p>This repository contains: </p> <ul> <li>28 sound stimuli (.wav)</li> <li>Dataset from the experiment as described in the paper (.RData)</li> <li>Dataset containing audio features as described in the paper (.RData)</li> <li>Analysis code in R (.pdf)</li> </ul> <p> </p>
Video Dataset of Respiratory Cilia Motion Phenotypes
<p>Respiratory cilia are important components in the lung defense mechanism. The coordinated beating of cilia cleans the airways of pathogens and foreign particles. We present a large-scale validation dataset of cilia motion for characterizing ciliary function. Ciliary beat frequency (CBF) is provided as benchmark metrics. The video dataset of cilia motion phenotypes contains four categories: temperatures, drugs and ACE2 manipulation. Under each category, mouse trachea samples were treated with different stimuli and imaged with a high-speed video microscope to acquire cilia motion. In addition, we generate ground truth masks labeling ciliary area for image segmentation. This validation dataset can serve as a benchmark for the computer vision community to develop models for analyzing ciliary beat pattern.</p> <p>This video dataset contains 872 videos and their ground-truth masks with the ciliary area labeled. The videos were recorded at 250 frames per second for 1 second. The image size is 800x800. Each pixel is 0.07987 μm. The csv file contains the CBF values of each video.</p>
Dataset of UAI 2021 Paper "An Unsupervised Video Game Playstyle Metric via State Discretization"
<p>This is a part of dataset of the paper published in UAI 2021 (37th Conference on Uncertainty in Artificial Intelligence).</p> <p>Including training datasets, testing datasets, and HSD models of three game platforms used in the paper: TORCS, RGSK, and Atari.</p> <p>The example program for using this file will be put on the author's github repo branch: <a href="https://github.com/DSobscure/cgi_drl_platform/tree/playstyle_uai2021">https://github.com/DSobscure/cgi_drl_platform/tree/playstyle_uai2021</a></p>
EMG and Video Dataset for sensor fusion based hand gestures recognition
<p>This dataset contains data for hand gesture recognition recorded with 3 different sensors. </p> <p>sEMG: recorded via the Myo armband that is composed of 8 equally spaced non-invasive sEMG sensors that can be placed approximately around the middle of the forearm. The sampling frequency of Myo is 200 Hz. The output of the Myo is a.u </p> <p>DVS: Dynamic Video Sensor which is a very low power event-based camera with 128x128 resolution</p> <p>DAVIS: Dynamic Video Sensor which is a very low power event-based camera with 240x180 resolution that also acquires APS frames.</p> <p>The dataset contains recordings of 21 subjects. Each subject performed 3 sessions, where each of the 5 hand gesture was recorded 5 times, each lasting for 2s. Between the gestures a relaxing phase of 1s is present where the muscles could go to the rest position, removing any residual muscular activation.</p> <p> </p> <p>Note: All the information for the DVS sensor has been extracted and can be found in the *.npy files. In case the raw data (.aedat) was needed please contact</p> <p> </p> <p>enea.ceolini@ini.uzh.ch</p> <p>elisa@ini.uzh.ch</p> <p>==== README ====</p> <p> </p> <p>DATASET STRUCTURE:</p> <p>EMG, DVS and APS recordings</p> <p>21 subjects</p> <p>3 sessions for each subject</p> <p>5 gestures in each session ('pinky', 'elle', 'yo', 'index', 'thumb')</p> <p> </p> <p>SINGLE DATASETS:</p> <p>- relax21_raw_emg.zip: contains raw sEMG and annotations (ground truth of gestures) in the format `subjectXX_sessionYY_ZZZ` with `XX` subject ID (01 to 21), `YY` session ID (01-03) and `ZZZ` that can be ‘emg’ or ‘ann’.</p> <p> </p> <p>- relax21_raw_dvs.zip: contains the full-frame dvs events in an array with dimensions 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity. The timestamps are in seconds and synchronized with the Myo. Each file is in the format `subjectXX_sessionYY_dvs` with `XX` subject ID (01 to 21), `YY` session ID (01-03).</p> <p> </p> <p>- relax21_cropped_aps.zip: contains the 40x40 pixel aps frames for all subjects and trials in the format `subjectXX_sessionYY_Z_W_K` with `XX` subject ID (01 to 21), `YY` session ID (01-03), Z gesture ('pinky', 'elle', 'yo', 'index', 'thumb’), W trial ID (1-5), `K` frame index.</p> <p> </p> <p>- relax21_cropped_dvs_emg_spikes.pkl: spiking dataset that can be used to reproduce the results in the paper. The dataset is a dictionary with the following keys:</p> <ul> <li><strong>- </strong><strong>y</strong>: array of size 1xN with the class (0->4).</li> <li><strong>- </strong><strong>sub</strong>: array of size 1xN with the subject id (1->10).</li> <li><strong>- </strong><strong>sess</strong>: array of size 1xN with the session id (1->3).</li> <li><strong>- </strong><strong>dvs</strong>: list of length N, each object in the list is a 2d array of size 4xT_n where T_n is the number of events in the trial and the 4 dimensions rappresent: 0 -> addr_x, 1 -> addr_y, 2 -> timestamp, 3 -> polarity .</li> <li><strong>- </strong><strong>emg</strong>: list of length N, each object in the list is a 2d array of size 3xT_n where T_n is the number of events in the trial and the 3 dimensions rappresent: 0 -> addr, 1 -> timestamp, 3 -> polarity.</li> </ul> <p> </p> <p> </p>
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>
Accompanying dataset for "Nappe oscillations on free-overfall structures, data from laboratory experiments (audio and video)"
<p>This dataset accompanies the manuscript "Nappe Oscillations on Free-Overfall Structures: Data from Laboratory Experiments" submitted to Scientific Data.</p> <p>This dataset contains raw audio and video data, which complement the dataset uploaded at: <a href="https://zenodo.org/record/3381078#.XlUGVSFKiUk">https://zenodo.org/record/3381078#.XlUGVSFKiUk</a></p> <p>The names of the folders describe each one of the 52 experiments, with respect to the submitted paper in Scientific Data:</p> <p>- M1 and M2 denote Model 1 and Model 2, respectively.</p> <p>- C and UC denote confined and unconfined nappe, respectively.</p> <p>- QR, THR, HR, R, and RR denote the crest type of the weir as explained in the paper.</p> <p>- W is the width of the crest and L is the falling height.</p>
Dataset to ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive Video Streaming – A Holistic Analysis"
<p>Dataset for the ACM MMSys'20 paper entitled "Comparing Fixed and Variable Segment Durations for Adaptive<br> Video Streaming – A Holistic Analysis".<br> The dataset includes</p> <ul> <li>Results from video encoding (using variable and fixed segment durations)</li> <li>Video sequences used for streaming evaluations</li> </ul>
A Dataset of User Generated Videos from Edinburgh Festival 2016
<p>Files include footage from performances of the trEd Dance group (edfest8, edfest9 and edfest10) and Rebecca Wilson’s ‘The Strawberry Show’ (edfest6 and edfest7) taken during Edinburgh Festival 2016 by BBC R&D as part of the COGNITUS project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687605. BBC©2016. Further details about the content are available in the accompanying paper <em>"An Open Access Dataset of User Generated Videos from Edinburgh Festival 2016"</em>. If you have any queries please contact BBC R&D at cognitus-h2020@rd.bbc.co.uk. This notice must remain attached to any copy of the content.</p> <p> </p>
ComplexVAD Video Anomaly Detection Dataset
<p><strong>Introduction</strong></p> <p>The ComplexVAD dataset consists of 104 training and 113 testing video sequences taken from a static camera looking at a scene of a two-lane street with sidewalks on either side of the street and another sidewalk going across the street at a crosswalk. The videos were collected over a period of a few months on the campus of the University of South Florida using a camcorder with 1920 x 1080 pixel resolution. Videos were collected at various times during the day and on each day of the week. Videos vary in duration with most being about 12 minutes long. The total duration of all training and testing videos is a little over 34 hours. The scene includes cars, buses and golf carts driving in two directions on the street, pedestrians walking and jogging on the sidewalks and crossing the street, people on scooters, skateboards and bicycles on the street and sidewalks, and cars moving in the parking lot in the background. Branches of a tree also move at the top of many frames.</p> <p>The 113 testing videos have a total of 118 anomalous events consisting of 40 different anomaly types.</p> <p>Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. A single frame can have more than one anomaly labeled.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~39GB</li> <li>The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity), a ground truth annotation file for each Test sequence, and a README.md file describing the data organization and ground truth annotation format.</li> <li>The zip files contain a Train directory, a Test directory, an annotations directory, and a README.md file.</li> </ul> <p><strong>License</strong></p> <p>The ComplexVAD dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
A dataset on the use of online video by students at the in-video level
<p><span>A dataset containing learning analytics data for the playback of learning materials in video format in different college courses in the STEM field, across a period of ten years. It can be used to test hypothesis and tools regarding the use of video in different learning environments, and should be of interest to the learning analytics and educational data mining communities. It can also be of help to teachers and other stakeholders in the educational process to take decisions based on learners actions when playing videos. It consists of data for 35 different videos, with a total of 40,453 sessions, and 313,724 records. The videos are accompanied by their timestamped transcription, both in the original language and their translation into English</span></p>
Pretrained features for Ekman6 and VideoEmotion8 video emotion datasets
Open the record for dataset details and reuse information.
Video-EEG Encoding-Decoding Dataset KU Leuven
<p><strong> If using this dataset, please cite the following paper and the current Zenodo repository.</strong></p> <p>This dataset is described in detail in the following paper:</p> <p><a href="https://iopscience.iop.org/article/10.1088/1741-2552/ad2333/meta">[1] Yao, Y., Stebner, A., Tuytelaars, T., Geirnaert, S., & Bertrand, A. (2024). Identifying temporal correlations between natural single-shot videos and EEG signals. <em>Journal of Neural Engineering</em>, <em>21</em>(1), 016018. doi:10.1088/1741-2552/ad2333</a></p> <p>The associated code is available at: <a href="https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file">https://github.com/YYao-42/Identifying-Temporal-Correlations-Between-Natural-Single-shot-Videos-and-EEG-Signals?tab=readme-ov-file</a></p> <h2><strong>Introduction</strong></h2> <p>The research work leading to this dataset was conducted at the Department of Electrical Engineering (ESAT), KU Leuven.</p> <p>This dataset contains electroencephalogram (EEG) data collected from 19 young participants with normal or corrected-to-normal eyesight when they were watching a series of carefully selected YouTube videos. The videos were muted to avoid the confounds introduced by audio. For synchronization, a square box was encoded outside of the original frames and flashed every 30 seconds in the top right corner of the screen. A photosensor, detecting the light changes from this flashing box, was affixed to that region using black tape to ensure that the box did not distract participants. The EEG data was recorded using a BioSemi ActiveTwo system at a sample rate of 2048 Hz. Participants wore a 64-channel EEG cap, and 4 electrooculogram (EOG) sensors were positioned around the eyes to track eye movements.</p> <p>The dataset includes a total of <strong>(19 subjects x 63 min + 9 subjects x 24 min)</strong> of data. Further details can be found in the following section.</p> <h2><strong>Content</strong></h2> <ul> <li>YouTube Videos: Due to copyright constraints, the dataset includes links to the original YouTube videos along with precise timestamps for the segments used in the experiments. The features proposed in [1] (<em>Object Flow</em>) have been extracted and can be downloaded here: <a href="https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing">https://drive.google.com/file/d/1J1tYrxVizrl1xP-W1imvlA_v-DPzZ2Qh/view?usp=sharing</a>.</li> <li>Raw EEG Data: Organized by subject ID, the dataset contains EEG segments corresponding to the presented videos. Both EEGLAB .set files (containing metadata) and .fdt files (containing raw data) are provided, which can also be read by popular EEG analysis Python packages such as MNE. <ul> <li>The naming convention links each EEG segment to its corresponding video. E.g., the EEG segment 01_eeg corresponds to video 01_Dance_1, 03_eeg corresponds to video 03_Acrob_1, Mr_eeg corresponds to video Mr_Bean, etc.</li> <li>The raw data have 68 channels. The first 64 channels are EEG data, and the last 4 channels are EOG data. The position coordinates of the standard BioSemi headcaps can be downloaded here: <a href="https://www.biosemi.com/download/Cap_coords_all.xls">https://www.biosemi.com/download/Cap_coords_all.xls</a>.</li> <li>Due to minor synchronization ambiguities, different clocks in the PC and EEG recorder, and missing or extra video frames during video playback (rarely occurred), the length of the EEG data may not perfectly match the corresponding video data. The difference, typically within a few milliseconds, can be resolved by truncating the modality with the excess samples.</li> </ul> </li> <li>Signal Quality Information: A supplementary .txt file detailing potential bad channels. Users can opt to create their own criteria for identifying and handling bad channels.</li> </ul> <p>The dataset is divided into two subsets: Single-shot and MrBean, based on the characteristics of the video stimuli.</p> <h3><strong>Single-shot Dataset</strong></h3> <p>The stimuli of this dataset consist of 13 single-shot videos (63 min in total), each depicting a single individual engaging in various activities such as dancing, mime, acrobatics, and magic shows. All the participants watched this video collection.</p> <table> <tbody> <tr> <th>Video ID</th> <th>Link</th> <th>Start time (s)</th> <th>End time (s)</th> </tr> </tbody> <tbody> <tr> <td>01_Dance_1</td> <td><a href="https://youtu.be/uOUVE5rGmhM">https://youtu.be/uOUVE5rGmhM</a></td> <td>8.54</td> <td>231.20</td> </tr> <tr> <td>03_Acrob_1</td> <td><a href="https://youtu.be/DjihbYg6F2Y">https://youtu.be/DjihbYg6F2Y</a></td> <td>4.24</td> <td>231.91</td> </tr> <tr> <td>04_Magic_1</td> <td><a href="https://youtu.be/CvzMqIQLiXE">https://youtu.be/CvzMqIQLiXE</a></td> <td>3.68</td> <td>348.17</td> </tr> <tr> <td>05_Dance_2</td> <td><a href="https://youtu.be/f4DZp0OEkK4">https://youtu.be/f4DZp0OEkK4</a></td> <td>5.05</td> <td>227.99</td> </tr> <tr> <td>06_Mime_2</td> <td><a href="https://youtu.be/u9wJUTnBdrs">https://youtu.be/u9wJUTnBdrs</a></td> <td>5.79</td> <td>347.05</td> </tr> <tr> <td>07_Acrob_2</td> <td><a href="https://youtu.be/kRqdxGPLajs">https://youtu.be/kRqdxGPLajs</a></td> <td>183.61</td> <td>519.27</td> </tr> <tr> <td>08_Magic_2</td> <td><a href="https://youtu.be/FUv-Q6EgEFI">https://youtu.be/FUv-Q6EgEFI</a></td> <td>3.36</td> <td>270.62</td> </tr> <tr> <td>09_Dance_3</td> <td><a href="https://youtu.be/LXO-jKksQkM">https://youtu.be/LXO-jKksQkM</a></td> <td>5.61</td> <td>294.17</td> </tr> <tr> <td>12_Magic_3</td> <td><a href="https://youtu.be/S84AoWdTq3E">https://youtu.be/S84AoWdTq3E</a></td> <td>1.76</td> <td>426.36</td> </tr> <tr> <td>13_Dance_4</td> <td><a href="https://youtu.be/0wc60tA1klw">https://youtu.be/0wc60tA1klw</a></td> <td>14.28</td> <td>217.18</td> </tr> <tr> <td>14_Mime_3</td> <td><a href="https://youtu.be/0Ala3ypPM3M">https://youtu.be/0Ala3ypPM3M</a></td> <td>21.87</td> <td>386.84</td> </tr> <tr> <td>15_Dance_5</td> <td><a href="https://youtu.be/mg6-SnUl0A0">https://youtu.be/mg6-SnUl0A0</a></td> <td>15.14</td> <td>233.85</td> </tr> <tr> <td>16_Mime_6</td> <td><a href="https://youtu.be/8V7rhAJF6Gc">https://youtu.be/8V7rhAJF6Gc</a></td> <td>31.64</td> <td>388.61</td> </tr> </tbody> </table> <h3><strong>MrBean Dataset</strong></h3> <p>Additionally, 9 participants watched an extra 24-minute clip from the first episode of Mr. Bean, where multiple (moving) objects may exist and interact, and the camera viewpoint may change. The subject IDs and the signal quality files are inherited from the single-shot dataset.</p> <table> <tbody> <tr> <th>Video ID</th> <th>Link</th> <th>Start time (s)</th> <th>End time (s)</th> </tr> </tbody> <tbody> <tr> <td>Mr_Bean</td> <td><a href="https://www.youtube.com/watch?v=7Im2I6STbms">https://www.youtube.com/watch?v=7Im2I6STbms</a></td> <td>39.77</td> <td>1495.00</td> </tr> </tbody> </table> <h2><strong>Acknowledgement</strong></h2> <p>This research is funded by the Research Foundation - Flanders (FWO) project No G081722N, junior postdoctoral fellowship fundamental research of the FWO (for S. Geirnaert, No. 1242524N), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 802895), the Flemish Government (AI Research Program), and the PDM mandate from KU Leuven (for S. Geirnaert, No PDMT1/22/009).</p> <p>We also thank the participants for their time and effort in the experiments.</p> <h2><strong>Contact Information</strong></h2> <p>Executive researcher: Yuanyuan Yao, <a href="mailto:yuanyuan.yao@kuleuven.be">yuanyuan.yao@kuleuven.be</a></p> <p>Led by: Prof. Alexander Bertrand, <a href="mailto:alexander.bertrand@kuleuven.be">alexander.bertrand@kuleuven.be</a></p> <p> </p>
Dataset about "An autonomous low‑cost studio to record production‑ready instructional videos"
<p>Dataset used in the article "An autonomous low‑cost studio to record production‑ready instructional videos".</p> <p>The dataset contains two excel files. One for the TAM model applied to the autonomous low-cost studio (called SAGA), and one with the student perceptions about the resulting videos.</p> <p>Much more information is available in the publication in the journal.</p>
Street Scene Video Anomaly Detection Dataset
<p><strong><span>Introduction</span></strong></p> <p><span>The Street Scene dataset consists of 46 training video sequences and 35 testing video sequences taken from a static USB camera looking down on a scene of a two-lane street with bike lanes and pedestrian sidewalks.<span> </span>Videos were collected from the camera at various times during two consecutive summers.<span> </span>All of the videos were taken during the daytime.<span> </span>The dataset is challenging because of the variety of activities taking place such as cars driving, turning, stopping and parking; pedestrians walking, jogging and pushing strollers; and bikers riding in bike lanes. In addition, the videos contain changing shadows, and moving background such as a flag and trees blowing in the wind.</span></p> <p><span>There are a total of 202,545 color video frames (56,135 for training and 146,410 for testing) each of size 1280 x 720 pixels. The frames were extracted from the original videos at 15 frames per second.</span></p> <p><span>The 35 testing sequences have a total of 205 anomalous events consisting of 17 different anomaly types. A complete list of anomaly types and the number of each in the test set can be found in our paper.</span></p> <p><span>Ground truth annotations are provided for each testing video in the form of bounding boxes around each anomalous event in each frame. Each bounding box is also labeled with a track number, meaning each anomalous event is labeled as a track of bounding boxes. Track lengths vary from tens of frames to 5200 which is the length of the longest testing sequence. A single frame can have more than one anomaly labeled.</span></p> <p><span>NOTE: This version of the dataset differs slightly with the original made available in 2020.<span> </span>Some anomalies were found in a few of the normal training sequences.<span> </span>These training frames were deleted from the dataset.<span> </span>Specifically, the following frames were removed:</span></p> <p><span>Train026: frames 1-184 (car taking a u-turn)</span></p> <p><span>Train027: frames 1-229 (jay walkers)</span></p> <p><span>Train031: frames 1-299 (jay walkers, illegally parked car)</span></p> <p><strong><span>At a Glance</span></strong></p> <ul> <li><span>The size of the unzipped dataset is ~46GB</span></li> <li><span>The dataset consists of Train sequences (containing only videos with normal activity), Test sequences (containing some anomalous activity) along with ground truth annotations, and a README.md file describing the data organization and ground truth annotation format.</span></li> <li><span>The zip file contains a Train directory, a Test directory and a README.md file.</span></li> </ul> <p><strong><span>Other Resources</span></strong></p> <p><span>None</span></p> <p><strong><span>Citation</span></strong></p> <p><span>If you use the Street Scene dataset in your research, please cite our contribution:</span></p> <pre><code>@inproceedings{ramachandra2020street, title={Street Scene: A new dataset and evaluation protocol for video anomaly detection}, author={Ramachandra, Bharathkumar and Jones, Michael}, booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}, pages={2569--2578}, year={2020} } </code></pre> <p><strong><span>License</span></strong></p> <p><span>The Street Scene dataset is released under </span><a href="https://creativecommons.org/licenses/by-sa/4.0/"><span>CC-BY-SA-4.0 license</span></a><span>.</span></p> <p><span>All data:</span></p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0 </code></pre>
Internet_Animal_Video_Dataset
<p>Anonymized dataset repository for our study entitled "Millions of pet videos deepen our understanding of human-cat interactions with implications for management." All the .csv files are encoded with UTF-8.</p> <p> </p> <p>CommentData_{category}.csv includes: </p> <p>Video_pub_time: Publishing time of the video;</p> <p>Video_id: The unique ID assigned to the video;</p> <p>Video_tag: The tags of the video;</p> <p>Video_play: The Play count of the video;</p> <p>Video_favor: The Favor count (another popularity metric) of the video;</p> <p>Video_duration: The duration of the video;</p> <p>Comment_text: The raw comment texts;</p> <p>Comment_emoji: The emoji used in the comment;</p> <p>Comment_like: The count of likes the comment received;</p> <p>Comment_gender: The self-selected gender of the commenter. </p> <p> </p> <p>VideoTagData_category.csv includes:</p> <p>Video_pub_time: Publishing time of the video;</p> <p>Video_id: The unique ID assigned to the video;</p> <p>Video_tag: The tags of the video;</p> <p>Video_play: The Play count of the video;</p> <p>Video_favor: The Favor count (another popularity metric) of the video;</p> <p>Video_duration: The duration of the video;</p> <p> </p>
Descriptive data from 265 Quantified Self Show&Tell videos - Qualitative dataset
<p>Descriptive data collected on 265 videos published on the Show&Tell projects archive of the Quantified Self website.</p> <p>Data include :</p> <ul> <li>The talk's characteristics : title, link, year and topic(s)</li> <li>Characteristics of the self-researcher(s) : origin, gender, number of participants in the project and if they are professional scientists</li> <li>Description of the data collection : items tracked, type and frequency of the collection, tools used</li> <li>The data exploration tools</li> <li>The duration of the project and if results where significant and given with insights</li> <li>Motivations for starting the project and reason to the end of it</li> <li>Additional comments</li> </ul>
CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR Videos
<p><a href="http://ieeexplore.ieee.org/document/9599346">CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360° Videos</a></p> <p><br> ## General Information<br> We develop the <a href="http://www.dis.cwi.nl/ceap-360vr-dataset/">CEAP-360VR</a> dataset to address the lack of continuously annotated behavioral and physiological datasets for 360 video VR affective computing. Accordingly, this dataset contains a) questionnaires (SSQ, IPQ, NASA-TLX); b) continuous valence-arousal annotations; c) head and eye movements as well as left and right eye pupil diameters while watching videos; d) peripheral physiological responses (ACC, EDA, SKT, BVP, HR, IBI). Our dataset also concludes the data pre-processing, data validating scripts, along with dataset description and key steps in the stage of data acquisition and pre-processing.</p> <p><br> ## Dataset Structure<br> The CEAP-360VR folder contains the following six subfolders</p> <p>1_Stimuli<br> 2_QuestionnaireData<br> 3_AnnotationData<br> 4_BehaviorData<br> 5_PhysioData<br> 6_Scripts<br> The following is a detailed description of each sub-file:</p> <p>1_Stimuli</p> <p> - VideoThumbNails<br> contains the eight thumbNails for each video (.jpg)<br> - VideoInfo.json<br> contains the detailed information for eight videos</p> <p>2_QuestionnaireData</p> <p> - PXX_Questionnaire_Data.json (X = 1, 2, ..., 32)<br> contains questionnaire data for each participant</p> <p>3_AnnotationData</p> <p> - Raw<br> contains the raw annotation data captured from the Joy-Con joystick for each participant<br> - Transformed<br> contains the transformed valence-arousal data generated from the raw data for each participant<br> - Frame<br> contains the re-sampled annotation data from the transformed data for each participant</p> <p>4_BehaviorData</p> <p> - Raw<br> contains the raw behavior data captured from the HTC VIVE Pro Eye Tobii Device for each participant<br> - Transformed<br> contains the transformed heam/eye movement data (pitch/yaw) generated from the raw data, as well as pupil diameter data for each participant<br> - Frame<br> contains the re-sampled behavior data generated from the transformed data for each participant<br> - HM_ScanPath<br> contains the head scanpath data generated from the transformed data for each participant<br> - EM_Fixation<br> contains the eye gaze fixation data generated from the transformed data for each participant</p> <p>5_PhysioData</p> <p> - Raw<br> contains the raw physiological data captured from the Empatica E4 wristband for each participant<br> - Transformed<br> contains the transformed physiological data generated from the raw data for each participant<br> - Frame<br> contains the re-sampled physiological data from the transformed data for each participant</p> <p>6_Scripts</p> <p> - Unity Project<br> contains the complete project of our user-controlled experiment (Unity 2018.4.1f1, HTC VIVE Pro Eye HMD)<br> - Data Processed<br> contains scripts that undertake the pre-processing steps for converting the raw data to the transformed/frame data in the transformed and frame folders.<br> conatins scripts for continuous annotation, behavior and physiological data analysis and visualization.<br> - CEAP-360VR_Baseline<br> contains scripts to generate processed behavioral and physiological data with V-A labels for deep learning experiments and features for machine learning experiments.<br> contains scripts to run ML and DL experiments under both subject-dependent and subject-independent model.</p> <p><br> ## Dataset Description<br> The CEAP-360VR Dataset [Description.pdf](https://github.com/cwi-dis/CEAP-360VR-Dataset/blob/master/CEAP-Dataset%20Description.pdf) introduces the dataset description and key steps in the stage of data acquisition and pre-processing.</p> <p><br> ## Dataset License<br> CEAP-360VR dataset is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license].</p> <p>## Citation</p> <p>Please cite our paper in any published work that uses this dataset as follows:<br> - Plain Text<br> T. Xue, A. El Ali, T. Zhang, G. Ding, and P. Cesar, "CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360° Videos," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3124080.</p> <p>- BibTex<br> @ARTICLE{Xue2021CEAP-360VR,<br> author={Xue, Tong and Ali, Abdallah El and Zhang, Tianyi and Ding, Gangyi and Cesar, Pablo},<br> journal={IEEE Transactions on Multimedia}, <br> title={CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360° Videos}, <br> year={2021},<br> volume={},<br> number={},<br> pages={1-1},<br> doi={https://doi.org/10.1109/TMM.2021.3124080}}</p> <p><br> ## Usage</p> <p> 1. We have performed the time alignment of different types of data and <br> videos for each participant, as well as the proceesing scripts that <br> can be used to generate both the transformed and frame data. <br> Researchers can run their analysis methods on them.<br> <br> 2. For researchers who want to try other data processing methods, you can directly use the raw data.</p> <p><br> ## About <br> The CEAP-360VR Dataset is maintained by Key Laboratory of Digital Performance and Simulation Technology at Beijing Institute of Technology and the Distributed & Interactive Systems (DIS) research group at Centrum Wiskunde & Informatica .</p> <p>Contact the authors<br> - Tong Xue: xuetong@bit.edu.cn, xue.tong@cwi.nl<br> - Abdallah El Ali: abdallah.el.ali@cwi.nl</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)
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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.