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15 results for “Egocentric”
EOAD (Egocentric Outdoor Activity Dataset)
<p>EOAD is a collection of videos captured by wearable cameras, mostly of sports activities. It contains both visual and audio modalities.</p> <p>It was initiated by the <a href="https://www.vision.huji.ac.il/egoseg/videos/dataset.html">HUJI</a> and <a href="https://github.com/azuxmioy/fpvsum">FPVSum</a> egocentric activity datasets. However, the number of samples and diversity of activities for HUJI and FPVSum were insufficient. Therefore, we combined these datasets and populated them with new YouTube videos.</p> <p>The selection of videos was based on the following criteria:</p> <ul> <li>The videos should not include text overlays.</li> <li>The videos should contain natural sound (no external music)</li> <li>The actions in videos should be continuous (no cutting the scene or jumping in time)</li> </ul> <p>Video samples were trimmed depending on scene changes for long videos (such as <em>driving</em>, <em>scuba diving</em>, and <em>cycling</em>). As a result, a video may have several clips depicting egocentric actions. Hence, video clips were extracted from carefully defined time intervals within videos. The final dataset includes video clips with a single action and natural audio information.</p> <p>Statistics for EOAD:</p> <ul> <li><strong>30</strong> activities</li> <li><strong>303</strong> distinct videos</li> <li><strong>1392</strong> video clips</li> <li><strong>2243</strong> minutes labeled videos clips</li> </ul> <p>The detailed statistics for the selected datasets and the crawled videos clips from YouTube are given below:</p> <ul> <li><strong><a href="https://www.vision.huji.ac.il/egoseg/videos/dataset.html">HUJI</a></strong>: 49 distinct videos - 148 video clips for 9 activities (<em>driving</em>, <em>biking</em>, <em>motorcycle</em>, <em>walking</em>, <em>boxing</em>, <em>horse riding</em>, <em>running</em>, <em>skiing</em>, <em>stair climbing</em>)</li> <li><strong><a href="https://github.com/azuxmioy/fpvsum">FPVSum</a></strong>: 39 distinct videos - 124 video segments for 8 activities (<em>biking</em>, <em>horse riding</em>, <em>skiing</em>, <em>longboarding</em>, <em>rock climbing</em>, <em>scuba</em>, <em>skateboarding</em>, <em>surfing</em>)</li> <li><strong>YouTube</strong>: 216 distinct videos - 1120 video clips for 27 activities (<em>american football</em>, <em>basketball</em>, <em>bungee jumping</em>, <em>driving</em>, <em>go-kart</em>, <em>horse riding</em>, <em>ice hockey</em>, <em>jet ski</em>, <em>kayaking</em>, <em>kitesurfing</em>, <em>longboarding</em>, <em>motorcycle</em>, <em>paintball</em>, <em>paragliding</em>, <em>rafting</em>, <em>rock climbing</em>, <em>rowing</em>, <em>running</em>, <em>sailing</em>, <em>scuba diving</em>, <em>skateboarding</em>, <em>soccer</em>, <em>stair climbing</em>, <em>surfing</em>, <em>tennis</em>, <em>volleyball</em>, <em>walking</em>)</li> </ul> <p>The video clips used for training, validation and test sets for each activity are listed in <em>Table 1</em>. Multiple video clips may belong to a single video because of trimming it for some reasons (i.e., scene cut, temporary overlayed text on videos, or video parts unrelated to activities).</p> <p>While splitting the dataset, the minimum number of videos for each activity was selected as 8. Additionally, the video samples were divided as 50%, 25%, and 25% for training (minimum four videos), validation (minimum two videos), and testing (minimum two videos), respectively. On the other hand, videos were split according to the raw video footage to prevent the mixing of similar video clips (having the same actors and scenes) into training, validation, and test sets. Therefore, we ensured that the video clips trimmed from the same videos were split together into training, validation, or test sets to satisfy a fair comparison.</p> <p>Some activities have continuity throughout the video, such as <em>scuba</em>, <em>longboarding</em>, or <em>riding horse</em>, which also have an equal number of video segments with the number of videos. However, some activities, such as skating, occurred in a short time, making the number of video segments higher than the others. As a result, the number of video clips for training, validation, and test sets was highly imbalanced for the selected activities (i.e., <em>jet ski</em> and <em>rafting</em> have 4; however, <em>soccer</em> has 99 video clips for training).</p> <p><strong> Table 1 - Dataset splitting for EOAD</strong></p> <table align="center"> <thead> <tr> <th> </th> <th> </th> <th> <p><strong>Train</strong></p> </th> <th> </th> <th> <p><strong>Validation</strong></p> </th> <th> </th> <th> <p><strong>Test</strong></p> </th> <th> </th> </tr> </thead> <tbody> <tr> <td> </td> <td> <p><strong>Action Label</strong></p> </td> <td> <p><strong>#Clips</strong></p> </td> <td> <p><strong>Total Duration</strong></p> </td> <td> <p><strong>#Clips</strong></p> </td> <td> <p><strong>Total Duration</strong></p> </td> <td> <p><strong>#Clips</strong></p> </td> <td> <p><strong>Total Duration</strong></p> </td> </tr> <tr> <td> </td> <td> <p><strong>AmericanFootball</strong></p> </td> <td> <p>34</p> </td> <td> <p>00:06:09</p> </td> <td> <p>36</p> </td> <td> <p>00:05:03</p> </td> <td> <p>9</p> </td> <td> <p>00:01:20</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Basketball</strong></p> </td> <td> <p>43</p> </td> <td> <p>01:13:22</p> </td> <td> <p>19</p> </td> <td> <p>00:08:13</p> </td> <td> <p>10</p> </td> <td> <p>00:28:46</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Biking</strong></p> </td> <td> <p>9</p> </td> <td> <p>01:58:01</p> </td> <td> <p>6</p> </td> <td> <p>00:32:22</p> </td> <td> <p>11</p> </td> <td> <p>00:36:16</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Boxing</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:24:54</p> </td> <td> <p>11</p> </td> <td> <p>00:14:14</p> </td> <td> <p>5</p> </td> <td> <p>00:17:30</p> </td> </tr> <tr> <td> </td> <td> <p><strong>BungeeJumping</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:02:22</p> </td> <td> <p>4</p> </td> <td> <p>00:01:36</p> </td> <td> <p>4</p> </td> <td> <p>00:01:31</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Driving</strong></p> </td> <td> <p>19</p> </td> <td> <p>00:37:23</p> </td> <td> <p>9</p> </td> <td> <p>00:24:46</p> </td> <td> <p>9</p> </td> <td> <p>00:29:23</p> </td> </tr> <tr> <td> </td> <td> <p><strong>GoKart</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:40:00</p> </td> <td> <p>3</p> </td> <td> <p>00:11:46</p> </td> <td> <p>3</p> </td> <td> <p>00:19:46</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Horseback</strong></p> </td> <td> <p>5</p> </td> <td> <p>01:15:14</p> </td> <td> <p>5</p> </td> <td> <p>01:02:26</p> </td> <td> <p>2</p> </td> <td> <p>00:20:38</p> </td> </tr> <tr> <td> </td> <td> <p><strong>IceHockey</strong></p> </td> <td> <p>52</p> </td> <td> <p>00:19:22</p> </td> <td> <p>46</p> </td> <td> <p>00:20:34</p> </td> <td> <p>10</p> </td> <td> <p>00:36:59</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Jetski</strong></p> </td> <td> <p>4</p> </td> <td> <p>00:23:35</p> </td> <td> <p>5</p> </td> <td> <p>00:18:42</p> </td> <td> <p>6</p> </td> <td> <p>00:02:43</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Kayaking</strong></p> </td> <td> <p>28</p> </td> <td> <p>00:43:11</p> </td> <td> <p>22</p> </td> <td> <p>00:14:23</p> </td> <td> <p>4</p> </td> <td> <p>00:11:05</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Kitesurfing</strong></p> </td> <td> <p>30</p> </td> <td> <p>00:21:51</p> </td> <td> <p>17</p> </td> <td> <p>00:05:38</p> </td> <td> <p>6</p> </td> <td> <p>00:01:32</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Longboarding</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:15:40</p> </td> <td> <p>4</p> </td> <td> <p>00:18:03</p> </td> <td> <p>4</p> </td> <td> <p>00:09:11</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Motorcycle</strong></p> </td> <td> <p>20</p> </td> <td> <p>00:49:38</p> </td> <td> <p>21</p> </td> <td> <p>00:13:53</p> </td> <td> <p>8</p> </td> <td> <p>00:20:30</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Paintball</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:33:52</p> </td> <td> <p>4</p> </td> <td> <p>00:12:08</p> </td> <td> <p>4</p> </td> <td> <p>00:08:52</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Paragliding</strong></p> </td> <td> <p>11</p> </td> <td> <p>00:28:42</p> </td> <td> <p>4</p> </td> <td> <p>00:10:16</p> </td> <td> <p>4</p> </td> <td> <p>00:19:50</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Rafting</strong></p> </td> <td> <p>4</p> </td> <td> <p>00:15:41</p> </td> <td> <p>3</p> </td> <td> <p>00:07:27</p> </td> <td> <p>3</p> </td> <td> <p>00:06:13</p> </td> </tr> <tr> <td> </td> <td> <p><strong>RockClimbing</strong></p> </td> <td> <p>6</p> </td> <td> <p>00:49:38</p> </td> <td> <p>2</p> </td> <td> <p>00:21:59</p> </td> <td> <p>2</p> </td> <td> <p>00:18:50</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Rowing</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:47:05</p> </td> <td> <p>3</p> </td> <td> <p>00:13:21</p> </td> <td> <p>3</p> </td> <td> <p>00:03:26</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Running</strong></p> </td> <td> <p>21</p> </td> <td> <p>01:21:56</p> </td> <td> <p>19</p> </td> <td> <p>00:46:29</p> </td> <td> <p>11</p> </td> <td> <p>00:42:59</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Sailing</strong></p> </td> <td> <p>7</p> </td> <td> <p>00:39:30</p> </td> <td> <p>4</p> </td> <td> <p>00:14:39</p> </td> <td> <p>6</p> </td> <td> <p>00:15:43</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Scuba</strong></p> </td> <td> <p>5</p> </td> <td> <p>00:35:02</p> </td> <td> <p>3</p> </td> <td> <p>00:23:43</p> </td> <td> <p>2</p> </td> <td> <p>00:18:52</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Skate</strong></p> </td> <td> <p>91</p> </td> <td> <p>00:15:53</p> </td> <td> <p>30</p> </td> <td> <p>00:07:01</p> </td> <td> <p>10</p> </td> <td> <p>00:02:03</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Ski</strong></p> </td> <td> <p>14</p> </td> <td> <p>01:48:15</p> </td> <td> <p>17</p> </td> <td> <p>01:01:59</p> </td> <td> <p>7</p> </td> <td> <p>00:39:15</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Soccer</strong></p> </td> <td> <p>102</p> </td> <td> <p>00:48:39</p> </td> <td> <p>52</p> </td> <td> <p>00:13:17</p> </td> <td> <p>16</p> </td> <td> <p>00:06:54</p> </td> </tr> <tr> <td> </td> <td> <p><strong>StairClimbing</strong></p> </td> <td> <p>6</p> </td> <td> <p>01:05:32</p> </td> <td> <p>6</p> </td> <td> <p>00:17:18</p> </td> <td> <p>5</p> </td> <td> <p>00:20:22</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Surfing</strong></p> </td> <td> <p>23</p> </td> <td> <p>00:12:51</p> </td> <td> <p>17</p> </td> <td> <p>00:06:52</p> </td> <td> <p>10</p> </td> <td> <p>00:07:04</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Tennis</strong></p> </td> <td> <p>34</p> </td> <td> <p>00:27:04</p> </td> <td> <p>9</p> </td> <td> <p>00:06:03</p> </td> <td> <p>9</p> </td> <td> <p>00:03:14</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Volleyball</strong></p> </td> <td> <p>87</p> </td> <td> <p>00:19:14</p> </td> <td> <p>35</p> </td> <td> <p>00:07:46</p> </td> <td> <p>7</p> </td> <td> <p>00:18:58</p> </td> </tr> <tr> <td> </td> <td> <p><strong>Walking</strong></p> </td> <td> <p>49</p> </td> <td> <p>00:43:02</p> </td> <td> <p>36</p> </td> <td> <p>00:38:25</p> </td> <td> <p>10</p> </td> <td> <p>00:10:23</p> </td> </tr> <tr> <td> <p><strong>Total</strong></p> </td> <td> <p>30</p> </td> <td> <p>740</p> </td> <td> <p>20:22:37</p> </td> <td> <p>452</p> </td> <td> <p>09:20:23</p> </td> <td> <p>200</p> </td> <td> <p>08:00:08</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>EOAD Code Repository</strong></p> <p>Scripts for downloading raw videos and trim them in to video clips are provided in <a href="https://github.com/maliarabaci/eoad">this GitHub</a> repository.</p> <p>Regarding the questions, please contact <strong><em>mali.arabaci@gmail.com.</em></strong></p>
The egocentric nature of action-sound associations
<p>These are the data of the corresponding manuscript: "The egocentric nature of action-sound associations" by Nicole Navolio, Guillaume Lemaitre, Alain Forget, and Laurie M. Heller.</p> <p>These are Matlab .mat files. Each file corresponds to one participant. There are four variables:</p> <p>- Accuracy: 0 = incorrect answer; 1= correct answer</p> <p>- Time: response time in ms. </p> <p>- Congruency: type of trial. 1 = incongruent, 2=congruent, 3=nosound</p> <p>- Order: trial order.</p> <p>Each variable is Nx2 matrix. N is the number of trials. First line is up gesture, second line is right gesture. </p>
Mussells Pires et al, Converting an allocentric goal into an egocentric steering signal [Data]
<p>Data for <em><a href="https://doi.org/10.1038/s41586-023-07006-3">Converting an allocentric goal into an egocentric steering signal</a>. </em></p>
Effect of egocentric and allocentric reference frames on spatial numerical associations
<p>From an embodied view of cognition, sensorimotor mechanisms are strongly involved in abstract processing, such as Arabic number meanings. For example, spatial cognition can influence number processing. These spatial-numerical associations (SNAs) have been deeply explored since the seminal spatial-numerical associations of response code (SNARC) effect (i.e., faster left/right sided responses to small/large magnitude numbers, respectively). While these SNAs along the transverse plane (left-to-right axis) have been extensively studied in cognitive sciences, no systematic assessment of other planes of the tridimensional space has been afforded. Moreover, there is no evidence of how SNAs organize themselves throughout the changes in spatial body-reference frames (egocentric and allocentric). Hence, this study aimed to explore how SNAs organize themselves along the transverse and sagittal planes when egocentric and allocentric changes are processed during body displacements in the environment. In the first experiment, the results revealed that when the participants used an egocentric reference, SNAs were observed only along the sagittal plane. In a second experiment that used an allocentric reference, the reversed pattern of results was observed: SNAs were present only along the transverse plane of the body. Overall, these findings suggest that depending on the spatial reference frames of the body, SNAs are strongly flexible.</p>
Entorhinal-retrosplenial circuits for allocentric-egocentric transformation of boundary coding
<p>Spatial navigation requires landmark coding from two perspectives, relying on viewpoint-invariant and self-referenced representations. The brain encodes information within each reference frame, but their interactions and functional dependency remains unclear. Here we investigate the relationship between neurons in rat retrosplenial cortex (RSC) and entorhinal cortex (MEC) that increase firing near boundaries of space. Border cells in RSC specifically encode walls, but not objects, and are sensitive to the animal's direction to nearby borders. These egocentric representations are generated independent of visual or whisker sensation, but depend on inputs from MEC that contains allocentric spatial cells. Pharmaco- and optogenetic inhibition of MEC cells led to a disruption of border coding in RSC, but not vice versa, indicating allocentric-to-egocentric transformation. Finally, RSC border cells fire prospective to the animal's next motion, unlike those in MEC, revealing the MEC-RSC pathway as an extended border coding circuit that implements coordinate transformation to guide navigation behavior.</p>
GS_EgoExo_Plaster Turning on Wheel_ML_EN 1 - videos_egocentric
<p>GS_EgoExo_Plaster Turning on Wheel_ML_EN 1 - videos_egocentric</p>
Activation of Human Visual Area V6 during Egocentric Navigation with and without visual Experience
<p>V6 is a retinotopic area located in the dorsal visual stream that integrates eye movements with retinal and visuo-motor signals. Despite the known role of V6 in visual motion, it is unknown whether it is involved in navigation and how sensory experiences shape its functional properties. We explored the involvement of V6 in egocentric navigation in sighted and in congenitally blind (CB) participants navigating via an in-house distance-to-sound sensory substitution device (SSD), the EyeCane. We performed two fMRI experiments on two independent datasets. In the first experiment, CB and sighted participants navigated the same mazes. The sighted performed the mazes via vision, while the CB via audition. The CB performed the mazes before and after a training session using the EyeCane SSD. In a second experiment a group of sighted people performed a motor topography task. Our results show that right V6 (rhV6) is selectively involved in egocentric navigation independently of the sensory modality used. Indeed, after training, rhV6 of CB is selectively recruited for auditory navigation, similar to rhV6 in the sighted. Moreover, we found activation for body movement in area V6, that can putatively contribute to its involvement in egocentric navigation. Taken together, our findings suggest that area rhV6 is a unique hub that transforms spatially relevant sensory information into an egocentric representation for navigation. While vision is clearly the dominant modality, rhV6 is in fact a supramodal area that can develop its selectivity for navigation in the absence of visual experience.</p>
Entorhinal-retrosplenial circuits for allocentric-egocentric transformation of boundary coding
Open the record for dataset details and reuse information.
Data from: Optimal switching between geocentric and egocentric strategies in navigation
Animals use a combination of egocentric navigation driven by the internal integration of environmental cues, interspersed with geocentric course correction and reorientation. These processes are accompanied by uncertainty in sensory acquisition of information, planning and execution. Inspired by observations of dung beetle navigational strategies that show switching between geocentric and egocentric strategies, we consider the question of optimal reorientation rates for the navigation of an agent moving along a preferred direction in the presence of multiple sources of noise. We address this using a model that takes the form of a correlated random walk at short time scales that is punctuated by reorientation events leading to a biased random walks at long time scales. This allows us to identify optimal alternation schemes and characterize their robustness in the context of noisy sensory acquisition as well as performance errors linked with variations in environmental conditions and agent–environment interactions.
Porcelain process raw videos egocentric part1
<p>Porcelain process raw videos egocentric part1</p>
Porcelain process raw videos egocentric part2
<p>Porcelain process raw videos egocentric part2</p>
Data from: Optimal switching between geocentric and egocentric strategies in navigation
Open the record for dataset details and reuse information.
EgoADL: Multimodal Daily-life Logging in Free-living Environments Using Non-Visual Egocentric Sensors on a Smartphone
<p>EgoADL dataset employs a commodity smartphone as an egocentric sensor hub, which captures the audio, wireless sensing signals (i.e., Wi-Fi), and motion sensor signals continuously. Users can perform arbitrary daily routines with the sensor hub, i.e., an in-pocket smartphone, in free-living environments. EgoADL will recognize ADLs including both human activity and human-object interaction from the sensor data without human intervention.</p>
Influence of Gravity on the Perception of Egocentric Distance (Blindpulling)
ClinicalTrials.gov study NCT02508545. IPD Sharing: Not stated. Countries: 1. Publications: 0.
UAVA (M2ED Egocentric View)
<p>The UAVA dataset us specifically designed for fostering applications which consider UAVs and humans as cooperative agents. We employ a real-world 3D scanned dataset (<a href="https://niessner.github.io/Matterport/">Matterport3D</a> ), physically-based shading, a gamified simulator for realistic drone navigation trajectory collection and randomized sampling, to generate multimodal data both from the user’s exocentric view of the drone, as well as the drone’s egocentric view.</p> <p>This is a subset of the UAVA dataset consisting of DJI M2ED drone renders (<strong>colour </strong>and <strong>depth</strong>) from an egocentric UAV view.</p>
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.