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authoradamhrv <adam@ahprojects.com>2019-04-12 09:09:13 +0200
committeradamhrv <adam@ahprojects.com>2019-04-12 09:09:13 +0200
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- <div class='site_name'>MegaPixels</div>
- <div class='splash'>UCCS</div>
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- <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'><span class="dataset-name">UnConstrained College Students</span> is a dataset of long-range surveillance photos of students at University of Colorado in Colorado Springs</span></div><div class='hero_subdesc'><span class='bgpad'>The UnConstrained College Students dataset includes 16,149 images and 1,732 identities of subjects on University of Colorado Colorado Springs campus and is used for making face recognition and face detection algorithms
-</span></div></div></section><section><div class='left-sidebar'><div class='meta'>
- <div class='gray'>Published</div>
- <div>2018</div>
- </div><div class='meta'>
- <div class='gray'>Images</div>
- <div>16,149 </div>
- </div><div class='meta'>
- <div class='gray'>Identities</div>
- <div>1,732 </div>
- </div><div class='meta'>
- <div class='gray'>Purpose</div>
- <div>Face recognition, face detection</div>
- </div><div class='meta'>
- <div class='gray'>Created by</div>
- <div>University of Colorado Colorado Springs (US)</div>
- </div><div class='meta'>
- <div class='gray'>Funded by</div>
- <div>ODNI, IARPA, ONR MURI, Amry SBIR, SOCOM SBIR</div>
- </div><div class='meta'>
- <div class='gray'>Website</div>
- <div><a href='http://vast.uccs.edu/Opensetface/' target='_blank' rel='nofollow noopener'>uccs.edu</a></div>
- </div></div><h2>UnConstrained College Students</h2>
-<p>[ page under development ]</p>
-<p>UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs. According to the authors of two papers associated with the dataset, subjects were "photographed using a long-range high-resolution surveillance camera without their knowledge" <a class="footnote_shim" name="[^funding_uccs]_1"> </a><a href="#[^funding_uccs]" class="footnote" title="Footnote 2">2</a>. To create the dataset, the researchers used a Canon 7D digital camera fitted with a Sigma 800mm telephoto lens and photographed students 150&ndash;200m away through their office window. Photos were taken during the morning and afternoon while students were walking to and from classes. The primary uses of this dataset are to train, validate, and build recognition and face detection algorithms for realistic surveillance scenarios.</p>
-<p>What makes the UCCS dataset unique is that it includes the highest resolution images of any publicly available face recognition dataset discovered so far (18MP), that it was captured on a campus without consent or awareness using a long-range telephoto lens, and that it was funded by United States defense and intelligence agencies.</p>
-<p>Combined funding sources for the creation of the initial and final release of the dataset include ODNI (Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. <a class="footnote_shim" name="[^funding_sb]_1"> </a><a href="#[^funding_sb]" class="footnote" title="Footnote 1">1</a> <a class="footnote_shim" name="[^funding_uccs]_2"> </a><a href="#[^funding_uccs]" class="footnote" title="Footnote 2">2</a></p>
-<p>In 2017 the UCCS face dataset was used for a defense and intelligence agency funded <a href="http://www.face-recognition-challenge.com/">face recognition challenge</a> at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was used for the <a href="https://erodner.github.io/ial2018eccv/">2nd Unconstrained Face Detection and Open Set Recognition Challenge</a> at the European Computer Vision Conference (ECCV) in Munich, Germany. Additional research projects that have used the UCCS dataset are included below in the list of verified citations.</p>
-</section><section>
- <h3>Who used UCCS?</h3>
-
- <p>
- This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries.
- </p>
-
- </section>
-
-<section class="applet_container">
-<!-- <div style="position: absolute;top: 0px;right: -55px;width: 180px;font-size: 14px;">Labeled Faces in the Wild Dataset<br><span class="numc" style="font-size: 11px;">20 citations</span>
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-<section>
-
- <h3>Biometric Trade Routes</h3>
-
- <p>
- To help understand how UCCS has been used around the world by commercial, military, and academic organizations; existing publicly available research citing UnConstrained College Students Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location.
- </p>
-
- </section>
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-<div class="caption">
- <ul class="map-legend">
- <li class="edu">Academic</li>
- <li class="com">Commercial</li>
- <li class="gov">Military / Government</li>
- </ul>
- <div class="source">Citation data is collected using <a href="https://semanticscholar.org" target="_blank">SemanticScholar.org</a> then dataset usage verified and geolocated.</div >
-</div>
-
-
-<section class="applet_container">
-
- <h3>Dataset Citations</h3>
- <p>
- The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms.
- </p>
-
- <div class="applet" data-payload="{&quot;command&quot;: &quot;citations&quot;}"></div>
-</section><section>
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- <div class="hr-wave-line hr-wave-line2"></div>
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-
- <h2>Supplementary Information</h2>
-
-</section><section><h3>Dates and Times</h3>
-<p>The images in UCCS were taken on 18 non-consecutive days during 2012&ndash;2013. Analysis of the <a href="assets/uccs_camera_exif.csv">EXIF data</a> embedded in original images reveal that most of the images were taken on Tuesdays, and the most frequent capture time throughout the week was 12:30PM.</p>
-</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_exif_plot_days.png' alt=' UCCS photos captured per weekday &copy; megapixels.cc'><div class='caption'> UCCS photos captured per weekday &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_exif_plot.png' alt=' UCCS photos captured per 10-minute intervals per weekday &copy; megapixels.cc'><div class='caption'> UCCS photos captured per 10-minute intervals per weekday &copy; megapixels.cc</div></div></section><section><div class='columns columns-2'><div class='column'><h4>UCCS photos taken in 2012</h4>
-<table>
-<thead><tr>
-<th>Date</th>
-<th>Photos</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Feb 23, 2012</td>
-<td>132</td>
-</tr>
-<tr>
-<td>March 6, 2012</td>
-<td>288</td>
-</tr>
-<tr>
-<td>March 8, 2012</td>
-<td>506</td>
-</tr>
-<tr>
-<td>March 13, 2012</td>
-<td>160</td>
-</tr>
-<tr>
-<td>March 20, 2012</td>
-<td>1,840</td>
-</tr>
-<tr>
-<td>March 22, 2012</td>
-<td>445</td>
-</tr>
-<tr>
-<td>April 3, 2012</td>
-<td>1,639</td>
-</tr>
-<tr>
-<td>April 12, 2012</td>
-<td>14</td>
-</tr>
-<tr>
-<td>April 17, 2012</td>
-<td>19</td>
-</tr>
-<tr>
-<td>April 24, 2012</td>
-<td>63</td>
-</tr>
-<tr>
-<td>April 25, 2012</td>
-<td>11</td>
-</tr>
-<tr>
-<td>April 26, 2012</td>
-<td>20</td>
-</tr>
-</tbody>
-</table>
-</div><div class='column'><h4>UCCS photos taken in 2013</h4>
-<table>
-<thead><tr>
-<th>Date</th>
-<th>Photos</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Jan 28, 2013</td>
-<td>1,056</td>
-</tr>
-<tr>
-<td>Jan 29, 2013</td>
-<td>1,561</td>
-</tr>
-<tr>
-<td>Feb 13, 2013</td>
-<td>739</td>
-</tr>
-<tr>
-<td>Feb 19, 2013</td>
-<td>723</td>
-</tr>
-<tr>
-<td>Feb 20, 2013</td>
-<td>965</td>
-</tr>
-<tr>
-<td>Feb 26, 2013</td>
-<td>736</td>
-</tr>
-</tbody>
-</table>
-</div></div></section><section><h3>Location</h3>
-<p>The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The <a href="https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1">original papers</a> also provides another clue: a <a href="https://www.semanticscholar.org/paper/Large-scale-unconstrained-open-set-face-database-Sapkota-Boult/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1/figure/1">picture of the camera</a> inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the <a href="https://www.google.com/maps/place/University+of+Colorado+Colorado+Springs/@38.8934297,-104.7992445,27a,35y,258.51h,75.06t/data=!3m1!1e3!4m5!3m4!1s0x87134fa088fe399d:0x92cadf3962c058c4!8m2!3d38.8968312!4d-104.8049528">location on Google Maps</a></p>
-</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_map.jpg' alt=' Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps'><div class='caption'> Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/uccs/assets/uccs_map_3d.jpg' alt=' 3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps'><div class='caption'> 3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps</div></div></section><section><h3>Funding</h3>
-<p>The UnConstrained College Students dataset is associated with two main research papers: "Large Scale Unconstrained Open Set Face Database" and "Unconstrained Face Detection and Open-Set Face Recognition Challenge". Collectively, these papers and the creation of the dataset have received funding from the following organizations:</p>
-<ul>
-<li>ONR (Office of Naval Research) MURI (The Department of Defense Multidisciplinary University Research Initiative) grant N00014-08-1-0638</li>
-<li>Army SBIR (Small Business Innovation Research) grant W15P7T-12-C-A210</li>
-<li>SOCOM (Special Operations Command) SBIR (Small Business Innovation Research) grant H92222-07-P-0020</li>
-<li>National Science Foundation Grant IIS-1320956</li>
-<li>ODNI (Office of Director of National Intelligence)</li>
-<li>IARPA (Intelligence Advance Research Projects Activity) R&amp;D contract 2014-14071600012</li>
-</ul>
-<h3>Opting Out</h3>
-<p>If you attended University of Colorado Colorado Springs and were captured by the long range surveillance camera used to create this dataset, there is unfortunately currently no way to be removed. The authors do not provide any options for students to opt-out nor were students informed they would be used for training face recognition. According to the authors, the lack of any consent or knowledge of participation is what provides part of the value of Unconstrained College Students Dataset.</p>
-<h3>Ethics</h3>
-<p>Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs <a href="https://www.uccs.edu/compliance/">Ethics and Compliance Office</a></p>
-<h3>Technical Details</h3>
-<p>For further technical information about the dataset, visit the <a href="https://vast.uccs.edu/Opensetface">UCCS dataset project page</a>.</p>
-<h2>Under Development</h2>
-<ul>
-<li>adding more verified locations to map and charts</li>
-<li>add EXIF file to CDN</li>
-</ul>
-</section><section><ul class="footnotes"><li><a name="[^funding_sb]" class="footnote_shim"></a><span class="backlinks"><a href="#[^funding_sb]_1">a</a></span><p>Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.</p>
-</li><li><a name="[^funding_uccs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^funding_uccs]_1">a</a><a href="#[^funding_uccs]_2">b</a></span><p>Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.</p>
-</li></ul></section>
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