From 8d8675821fa307088c5be5ae4a72ec89da0ee747 Mon Sep 17 00:00:00 2001
From: adamhrv <adam@ahprojects.com>
Date: Mon, 8 Apr 2019 12:10:30 +0200
Subject: update duke

---
 site/public/datasets/brainwash/index.html          |  1 -
 site/public/datasets/duke_mtmc/index.html          | 32 ++++++++++++----------
 site/public/datasets/oxford_town_centre/index.html | 18 +++++-------
 3 files changed, 25 insertions(+), 26 deletions(-)

(limited to 'site')

diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html
index c367d8b1..240ec499 100644
--- a/site/public/datasets/brainwash/index.html
+++ b/site/public/datasets/brainwash/index.html
@@ -114,7 +114,6 @@
   
 </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_saliency_map.jpg' alt=' A visualization of 81,973 head annotations from the Brainwash dataset training partition. &copy; megapixels.cc'><div class='caption'> A visualization of 81,973 head annotations from the Brainwash dataset training partition. &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/00425000_960.jpg' alt=' An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/brainwash/assets/brainwash_montage.jpg' alt=' 49 of the 11,918 images included in the Brainwash dataset. License: Open Data Commons Public Domain Dedication (PDDL)'><div class='caption'> 49 of the 11,918 images included in the Brainwash dataset. License: Open Data Commons Public Domain Dedication (PDDL)</div></div></section><section><p>TODO</p>
 <ul>
-<li>include the images referenced in the chinese defence papers?</li>
 <li>change supp images to 2x2 grid with bboxes</li>
 <li>add bounding boxes to the header image</li>
 <li>remake montage with randomized images, with bboxes</li>
diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html
index 06a9ed1b..8ff4ef43 100644
--- a/site/public/datasets/duke_mtmc/index.html
+++ b/site/public/datasets/duke_mtmc/index.html
@@ -47,9 +47,11 @@
     <div><a href='http://vision.cs.duke.edu/DukeMTMC/' target='_blank' rel='nofollow noopener'>duke.edu</a></div>
   </div></div><h2>Duke MTMC</h2>
 <p>[ page under development ]</p>
-<p>The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of video recorded on Duke University campus during for the purpose of training, evaluating, and improving <em>multi-target multi-camera tracking</em> for surveillance. The dataset includes over 14 hours of 1080p video from 8 cameras positioned around Duke's campus during February and March 2014. Over 2,700 unique people are included in the dataset, which has become of the most widely used person re-identification image datasets.</p>
-<p>The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy".</p>
-</section><section>
+<p>The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking is used for citywide dragnet surveillance systems such as those used throughout China by SenseTime<a class="footnote_shim" name="[^sensetime_qz]_1"> </a><a href="#[^sensetime_qz]" class="footnote" title="Footnote 1">1</a> and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets<a class="footnote_shim" name="[^sensenets_uyghurs]_1"> </a><a href="#[^sensenets_uyghurs]" class="footnote" title="Footnote 2">2</a>. In fact researchers from both SenseTime<a class="footnote_shim" name="[^sensetime1]_1"> </a><a href="#[^sensetime1]" class="footnote" title="Footnote 4">4</a> <a class="footnote_shim" name="[^sensetime2]_1"> </a><a href="#[^sensetime2]" class="footnote" title="Footnote 5">5</a> and SenseNets<a class="footnote_shim" name="[^sensenets_sensetime]_1"> </a><a href="#[^sensenets_sensetime]" class="footnote" title="Footnote 3">3</a> used the Duke MTMC dataset for their research.</p>
+<p>The Duke MTMC dataset is unique because it is the largest publicly available MTMC and person re-identification dataset and has the longest duration of annotated video. In total, the Duke MTMC dataset provides over 14 hours of 1080p video from 8 synchronized surveillance cameras.<a class="footnote_shim" name="[^duke_mtmc_orig]_1"> </a><a href="#[^duke_mtmc_orig]" class="footnote" title="Footnote 6">6</a> It is among the most widely used person re-identification datasets in the world. The approximately 2,700 unique people in the Duke MTMC videos, most of whom are students, are used for research and development of surveillance technologies by commercial, academic, and even defense organizations.</p>
+</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_reid_montage.jpg' alt=' A collection of 1,600 out of the 2,700 students captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension. &copy; megapixels.cc'><div class='caption'> A collection of 1,600 out of the 2,700 students captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension. &copy; megapixels.cc</div></div></section><section><p>The creation and publication of the Duke MTMC dataset in 2016 was originally funded by the U.S. Army Research Laboratory and the National Science Foundation<a class="footnote_shim" name="[^duke_mtmc_orig]_2"> </a><a href="#[^duke_mtmc_orig]" class="footnote" title="Footnote 6">6</a>. Since 2016 use of the Duke MTMC dataset images have been publicly acknowledged in research funded by or on behalf of the Chinese National University of Defense<a class="footnote_shim" name="[^cn_defense1]_1"> </a><a href="#[^cn_defense1]" class="footnote" title="Footnote 7">7</a><a class="footnote_shim" name="[^cn_defense2]_1"> </a><a href="#[^cn_defense2]" class="footnote" title="Footnote 8">8</a>, IARPA and IBM<a class="footnote_shim" name="[^iarpa_ibm]_1"> </a><a href="#[^iarpa_ibm]" class="footnote" title="Footnote 9">9</a>, and U.S. Department of Homeland Security<a class="footnote_shim" name="[^us_dhs]_1"> </a><a href="#[^us_dhs]" class="footnote" title="Footnote 10">10</a>.</p>
+<p>The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy".<a class="footnote_shim" name="[^duke_mtmc_orig]_3"> </a><a href="#[^duke_mtmc_orig]" class="footnote" title="Footnote 6">6</a> Camera 7 and 2 capture large groups of prospective students and children. Camera 5 was positioned to capture students as they enter and exit Duke University's main chapel.  Each camera's location is documented below.</p>
+</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_camera_map.jpg' alt=' Duke MTMC camera locations on Duke University campus &copy; megapixels.cc'><div class='caption'> Duke MTMC camera locations on Duke University campus &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_cameras.jpg' alt=' Duke MTMC camera views for 8 cameras deployed on campus &copy; megapixels.cc'><div class='caption'> Duke MTMC camera views for 8 cameras deployed on campus &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliencies.jpg' alt=' Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus &copy; megapixels.cc'><div class='caption'> Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus &copy; megapixels.cc</div></div></section><section>
   <h3>Who used Duke MTMC Dataset?</h3>
 
   <p>
@@ -109,17 +111,19 @@
 
   <h2>Supplementary Information</h2>
   
-</section><section><h4>Data Visualizations</h4>
-</section><section><div class='columns columns-2'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam1.jpg' alt=' Camera 1 &copy; megapixels.cc'><div class='caption'> Camera 1 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam2.jpg' alt=' Camera 2 &copy; megapixels.cc'><div class='caption'> Camera 2 &copy; megapixels.cc</div></div></section></div></section><section><div class='columns columns-2'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam3.jpg' alt=' Camera 3 &copy; megapixels.cc'><div class='caption'> Camera 3 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam4.jpg' alt=' Camera 4 &copy; megapixels.cc'><div class='caption'> Camera 4 &copy; megapixels.cc</div></div></section></div></section><section><div class='columns columns-2'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam5.jpg' alt=' Camera 5 &copy; megapixels.cc'><div class='caption'> Camera 5 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam6.jpg' alt=' Camera 6 &copy; megapixels.cc'><div class='caption'> Camera 6 &copy; megapixels.cc</div></div></section></div></section><section><div class='columns columns-2'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam7.jpg' alt=' Camera 7 &copy; megapixels.cc'><div class='caption'> Camera 7 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam8.jpg' alt=' Camera 8 &copy; megapixels.cc'><div class='caption'> Camera 8 &copy; megapixels.cc</div></div></section></div></section><section><h3>Alternate Layout</h3>
-</section><section><div class='columns columns-4'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam1.jpg' alt=' Camera 1 &copy; megapixels.cc'><div class='caption'> Camera 1 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam2.jpg' alt=' Camera 2 &copy; megapixels.cc'><div class='caption'> Camera 2 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam3.jpg' alt=' Camera 3 &copy; megapixels.cc'><div class='caption'> Camera 3 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam4.jpg' alt=' Camera 4 &copy; megapixels.cc'><div class='caption'> Camera 4 &copy; megapixels.cc</div></div></section></div></section><section><div class='columns columns-4'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam5.jpg' alt=' Camera 5 &copy; megapixels.cc'><div class='caption'> Camera 5 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam6.jpg' alt=' Camera 6 &copy; megapixels.cc'><div class='caption'> Camera 6 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam7.jpg' alt=' Camera 7 &copy; megapixels.cc'><div class='caption'> Camera 7 &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/duke_mtmc/assets/duke_mtmc_saliency_cam8.jpg' alt=' Camera 8 &copy; megapixels.cc'><div class='caption'> Camera 8 &copy; megapixels.cc</div></div></section></div></section><section><h3>TODO</h3>
-<ul>
-<li>expand story</li>
-<li>add google street view images of each camera location?</li>
-<li>add actual head detections to header image with faces blurred</li>
-<li>add 4 diverse example images with faces blurred</li>
-<li>add links to google map locations of each camera</li>
-</ul>
-</section>
+</section><section><h3>Notes</h3>
+<p>The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812</p>
+</section><section><ul class="footnotes"><li><a name="[^sensetime_qz]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensetime_qz]_1">a</a></span><p><a href="https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/">https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/</a></p>
+</li><li><a name="[^sensenets_uyghurs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensenets_uyghurs]_1">a</a></span><p><a href="https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/">https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/</a></p>
+</li><li><a name="[^sensenets_sensetime]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensenets_sensetime]_1">a</a></span><p>"Attention-Aware Compositional Network for Person Re-identification". 2018. <a href="https://www.semanticscholar.org/paper/Attention-Aware-Compositional-Network-for-Person-Xu-Zhao/14ce502bc19b225466126b256511f9c05cadcb6e">Source</a></p>
+</li><li><a name="[^sensetime1]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensetime1]_1">a</a></span><p>"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. <a href="https://www.semanticscholar.org/paper/End-to-End-Deep-Kronecker-Product-Matching-for-Shen-Xiao/947954cafdefd471b75da8c3bb4c21b9e6d57838">source</a></p>
+</li><li><a name="[^sensetime2]" class="footnote_shim"></a><span class="backlinks"><a href="#[^sensetime2]_1">a</a></span><p>"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. <a href="https://www.semanticscholar.org/paper/Person-Re-identification-with-Deep-Graph-Neural-Shen-Li/08d2a558ea2deb117dd8066e864612bf2899905b">Source</a></p>
+</li><li><a name="[^duke_mtmc_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^duke_mtmc_orig]_1">a</a><a href="#[^duke_mtmc_orig]_2">b</a><a href="#[^duke_mtmc_orig]_3">c</a></span><p>"Performance Measures and a Data Set for</p>
+</li><li><a name="[^cn_defense1]" class="footnote_shim"></a><span class="backlinks"><a href="#[^cn_defense1]_1">a</a></span><p>"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. <a href="https://www.semanticscholar.org/paper/Tracking-by-Animation%3A-Unsupervised-Learning-of-He-Liu/e90816e1a0e14ea1e7039e0b2782260999aef786">Source</a></p>
+</li><li><a name="[^cn_defense2]" class="footnote_shim"></a><span class="backlinks"><a href="#[^cn_defense2]_1">a</a></span><p>"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. <a href="https://www.semanticscholar.org/paper/Unsupervised-Multi-Object-Detection-for-Video-Using-He-He/59f357015054bab43fb8cbfd3f3dbf17b1d1f881">Source</a></p>
+</li><li><a name="[^iarpa_ibm]" class="footnote_shim"></a><span class="backlinks"><a href="#[^iarpa_ibm]_1">a</a></span><p>"Horizontal Pyramid Matching for Person Re-identification". 2019. <a href="https://www.semanticscholar.org/paper/Horizontal-Pyramid-Matching-for-Person-Fu-Wei/c2a5f27d97744bc1f96d7e1074395749e3c59bc8">Source</a></p>
+</li><li><a name="[^us_dhs]" class="footnote_shim"></a><span class="backlinks"><a href="#[^us_dhs]_1">a</a></span><p>"Re-Identification with Consistent Attentive Siamese Networks". 2018. <a href="https://www.semanticscholar.org/paper/Re-Identification-with-Consistent-Attentive-Siamese-Zheng-Karanam/24d6d3adf2176516ef0de2e943ce2084e27c4f94">Source</a></p>
+</li></ul></section>
 
   </div>
   <footer>
diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html
index 3f2a698a..cda1cde5 100644
--- a/site/public/datasets/oxford_town_centre/index.html
+++ b/site/public/datasets/oxford_town_centre/index.html
@@ -46,9 +46,8 @@
     <div class='gray'>Website</div>
     <div><a href='http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html' target='_blank' rel='nofollow noopener'>ox.ac.uk</a></div>
   </div></div><h2>Oxford Town Centre</h2>
-<p>[ page under development ]</p>
-<p>The Oxford Town Centre dataset is a video of pedestrians in a busy downtown area in Oxford used for creating surveillance algorithms with potential applications in remote biometric analysis and non-cooperative face recognition.<a class="footnote_shim" name="[^ben_benfold_orig]_1"> </a><a href="#[^ben_benfold_orig]" class="footnote" title="Footnote 1">1</a> The dataset was originally created to build algorithms that improve the stability of pedestrian detectors to provide more accurate head location estimates, leading to more accurate face recognition.</p>
-<p>Oxford Town Centre dataset is unique in that it uses footage from a public CCTV camera that is designated for public safety. Since its publication in 2009, the Oxford Town Centre CCTV footage dataset, and all 2,200 people in the video, have been redistributed around the world for the purpose of surveillance research and development. There are over 80 verified research projects that have used the Oxford Town Centre dataset. The usage even extends to commercial organizations including Amazon, Disney, and OSRAM.</p>
+<p>The Oxford Town Centre dataset is a CCTV video of pedestrians in a busy downtown area in Oxford used for research and development of activity and face recognition systems.<a class="footnote_shim" name="[^ben_benfold_orig]_1"> </a><a href="#[^ben_benfold_orig]" class="footnote" title="Footnote 1">1</a> The CCTV video was obtained from a public surveillance camera at the corner of Cornmarket and Market St. in Oxford, England and includes approximately 2,200 people. Since its publication in 2009<a class="footnote_shim" name="[^guiding_surveillance]_1"> </a><a href="#[^guiding_surveillance]" class="footnote" title="Footnote 2">2</a> the Oxford Town Centre dataset has been used in over 80 verified research projects including commercial research by Amazon, Disney, OSRAM, and Huawei; and academic research in China, Israel, Russia, Singapore, the US, and Germany among tensomes more.</p>
+<p>The Oxford Town Centre dataset is unique in that it uses footage from a public surveillance camera that would otherwise be designated for public safety. The video shows that the pedestrians act normally and unrehearsed indicating lack of consent or notice of participation in the research project.</p>
 </section><section>
   <h3>Who used TownCentre?</h3>
 
@@ -110,8 +109,10 @@
   <h2>Supplementary Information</h2>
   
 </section><section><h3>Location</h3>
-<p>The street location of the camera used for the Oxford Town Centre dataset can be easily confirmed using only two visual clues in video: the GAP store and the main road <a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">source</a>. The camera angle and field of view indicate that the camera was elevated and placed at the corner. The edge of the building is visible and there is a small white nylon strap and pigeon deterrent spikes visible on the upper perimeter of the building. The field of view indicates the camera uses a wide angle lens. Combined with the camera's stability and pigeon appearances in front of the camera at 1:24 and 3:29, these visual cues indicate that the camera was mounted outside on the corner of the building just above the deterrence spikes.</p>
-</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/oxford_town_centre/assets/oxford_town_centre_cctv.jpg' alt=' Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View and Oxford Town Centre dataset'><div class='caption'> Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View and Oxford Town Centre dataset</div></div></section><section><h3>Demo Videos Using Oxford Town Centre Dataset</h3>
+<p>The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs <a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">source</a>. At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their <a href="http://www.robots.ox.ac.uk/ActiveVision/Research/Projects/2009bbenfold_headpose/project.html">research projects</a> it is likely that they would also be able to access to this camera.</p>
+<p>To discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera <a href="https://www.oxcivicsoc.org.uk/northgate-house-cornmarket/">pointing in the same direction</a> as the Oxford Town Centre dataset proving the camera can and has been rotated before.</p>
+<p>As for the capture date, the text on the storefront display shows a sale happening from December 2nd &ndash; 7th indicating the capture date was between or just before those dates. The capture year is either 2008 or 2007 since prior to 2007 the Carphone Warehouse (<a href="https://www.flickr.com/photos/katieportwin/364492063/in/photolist-4meWFE-yd7rw-yd7X6-5sDHuc-yd7DN-59CpEK-5GoHAc-yd7Zh-3G2uJP-yd7US-5GomQH-4peYpq-4bAEwm-PALEr-58RkAp-5pHEkf-5v7fGq-4q1J9W-4kypQ2-5KX2Eu-yd7MV-yd7p6-4McgWb-5pJ55w-24N9gj-37u9LK-4FVcKQ-a81Enz-5qNhTG-59CrMZ-2yuwYM-5oagH5-59CdsP-4FVcKN-4PdxhC-5Lhr2j-2PAd2d-5hAwvk-zsQSG-4Cdr4F-3dUPEi-9B1RZ6-2hv5NY-4G5qwP-HCHBW-4JiuC4-4Pdr9Y-584aEV-2GYBEc-HCPkp/">photo</a>, <a href="http://www.oxfordhistory.org.uk/cornmarket/west/47_51.html">history</a>) did not exist at this location. Since the sweaters in the GAP window display are more similar to those in a <a href="web.archive.org/web/20081201002524/http://www.gap.com/">GAP website snapshot</a> from November 2007, our guess is that the footage was obtained during late November or early December 2007. The lack of street vendors and slight waste residue near the bench suggests that is was probably a weekday after rubbish removal.</p>
+</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/oxford_town_centre/assets/oxford_town_centre_cctv.jpg' alt=' Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (<a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">map</a>)'><div class='caption'> Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (<a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">map</a>)</div></div></section><section><div class='columns columns-'><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/oxford_town_centre/assets/oxford_town_centre_sal_body.jpg' alt=' Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset &copy; megapixels.cc'><div class='caption'> Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset &copy; megapixels.cc</div></div></section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/oxford_town_centre/assets/oxford_town_centre_sal_face.jpg' alt=' Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset &copy; megapixels.cc'><div class='caption'> Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset &copy; megapixels.cc</div></div></section></div></section><section><h3>Demo Videos Using Oxford Town Centre Dataset</h3>
 <p>Several researchers have posted their demo videos using the Oxford Town Centre dataset on YouTube:</p>
 <ul>
 <li><a href="https://www.youtube.com/watch?v=nO-3EM9dEd4">Multi target tracking on Oxford Dataset</a></li>
@@ -121,13 +122,8 @@
 <li><a href="https://www.youtube.com/watch?v=ErLtfUAJA8U">towncentre</a></li>
 <li><a href="https://www.youtube.com/watch?v=LwMOmqvhnoc">VTD - towncenter.avi</a></li>
 </ul>
-<p>TODO</p>
-<ul>
-<li>make heatmap viz</li>
-<li>add license info</li>
-</ul>
 </section><section><ul class="footnotes"><li><a name="[^ben_benfold_orig]" class="footnote_shim"></a><span class="backlinks"><a href="#[^ben_benfold_orig]_1">a</a></span><p>Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.</p>
-</li><li><a name="[^guiding_surveillance]" class="footnote_shim"></a><span class="backlinks"></span><p>"Guiding Visual Surveillance by Tracking Human Attention". 2009.</p>
+</li><li><a name="[^guiding_surveillance]" class="footnote_shim"></a><span class="backlinks"><a href="#[^guiding_surveillance]_1">a</a></span><p>"Guiding Visual Surveillance by Tracking Human Attention". 2009.</p>
 </li></ul></section>
 
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