never say final, update uccs

1 files changed, 18 insertions, 110 deletions

diff --git a/site/content/pages/datasets/duke_mtmc/index.md b/site/content/pages/datasets/duke_mtmc/index.md
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--- a/site/content/pages/datasets/duke_mtmc/index.md
+++ b/site/content/pages/datasets/duke_mtmc/index.md
@@ -1,7 +1,7 @@
 ------------
 
 status: published
-title: Duke Multi-Target, Multi-Camera Tracking
+title: Duke MTMC
 desc: <span class="dataset-name">Duke MTMC</span> is a dataset of surveillance camera footage of students on Duke University campus
 subdesc: Duke MTMC contains over 2 million video frames and 2,000 unique identities collected from 8 HD cameras at Duke University campus in March 2014
 slug: duke_mtmc
@@ -14,124 +14,32 @@ authors: Adam Harvey
 ------------
 
 ### sidebar
+### end sidebar
 
-+ Created: 2014
-+ Identities: Over 2,700
-+ Used for: Face recognition, person re-identification
-+ Created by: Computer Science Department, Duke University, Durham, US
-+ Website: <a href="http://vision.cs.duke.edu/DukeMTMC/">duke.edu</a>
+## Duke MTMC
 
-## Duke Multi-Target, Multi-Camera Tracking Dataset (Duke MTMC)
+[ page under development ]
 
-[ PAGE UNDER DEVELOPMENT ]
+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 *multi-target multi-camera tracking* 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.
 
-Duke MTMC is a dataset of video recorded on Duke University campus during for the purpose of training, evaluating, and improving *multi-target multi-camera tracking*. The videos were recorded during February and March 2014 and cinclude
+The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 
 
-Includes a total of 888.8 minutes of video (ind. verified)
-
-"We make available a new data set that has more than 2 million frames and more than 2,700 identities. It consists of 8×85 minutes of 1080p video recorded at 60 frames per second from 8 static cameras deployed on the Duke University campus during periods between lectures, when pedestrian traffic is heavy."
-
-The dataset includes approximately 2,000 annotated identities appearing in 85 hours of video from 8 cameras located throughout Duke University's campus.
-
-![caption: Duke MTMC pixel-averaged image of camera #5 is shown with the bounding boxes for each student drawn in white. (c) Adam Harvey](assets/duke_mtmc_cam5_average_comp.jpg)
-
-According to the dataset authors, 
-
-{% include 'map.html' %}
-
-{% include 'chart.html' %}
-
-{% include 'piechart.html' %}
+{% include 'dashboard.html' %}
 
 {% include 'supplementary_header.html' %}
 
-{% include 'citations.html' %}
-
-
-----
-
-## Research Notes
-
-- "We make available a new data set that has more than 2 million frames and more than 2,700 identities. It consists of 8×85 minutes of 1080p video recorded at 60 frames per second from 8 static cameras deployed on the Duke University campus during periods between lectures, when pedestrian traffic is heavy." - 27a2fad58dd8727e280f97036e0d2bc55ef5424c
-- "This work was supported in part by the EPSRC Programme Grant (FACER2VM) EP/N007743/1, EPSRC/dstl/MURI project EP/R018456/1, the National Natural Science Foundation of China (61373055, 61672265, 61602390, 61532009, 61571313), Chinese Ministry of Education (Z2015101), Science and Technology Department of Sichuan Province (2017RZ0009 and 2017FZ0029), Education Department of Sichuan Province (15ZB0130), the Open Research Fund from Province Key Laboratory of Xihua University (szjj2015-056) and the NVIDIA GPU Grant Program." - ec9c20ed6cce15e9b63ac96bb5a6d55e69661e0b
-- "DukeMTMC aims to accelerate advances in multi-target multi-camera tracking. It provides a tracking system that works within and across cameras, a new large scale HD video data set recorded by 8 synchronized cameras with more than 7,000 single camera trajectories and over 2,000 unique identities, and a new performance evaluation method that measures how often a system is correct about who is where"
-- DukeMTMC is a new, manually annotated, calibrated, multi-camera data set recorded outdoors on the Duke University campus with 8 synchronized cameras. It consists of:
-
-    8 static cameras x 85 minutes of 1080p 60 fps video
-    More than 2,000,000 manually annotated frames
-    More than 2,000 identities
-    Manual annotation by 5 people over 1 year
-    More identities than all existing MTMC datasets combined
-    Unconstrained paths, diverse appearance
-- 
-DukeMTMC Project
-Ergys Ristani Ergys Ristani Ergys Ristani Ergys Ristani Ergys Ristani
-
-People involved:
-Ergys Ristani, Francesco Solera, Roger S. Zou, Rita Cucchiara, Carlo Tomasi.
-
-Navigation:
+#### Data Visualizations
 
-    Data Set
-    Downloads
-    Downloads
-    Dataset Extensions
-    Performance Measures
-    Tracking Systems
-    Publications
-    How to Cite
-    Contact
-
-Welcome to the Duke Multi-Target, Multi-Camera Tracking Project.
-
-DukeMTMC aims to accelerate advances in multi-target multi-camera tracking. It provides a tracking system that works within and across cameras, a new large scale HD video data set recorded by 8 synchronized cameras with more than 7,000 single camera trajectories and over 2,000 unique identities, and a new performance evaluation method that measures how often a system is correct about who is where.
-DukeMTMC Data Set
-Snapshot from the DukeMTMC data set.
-
-DukeMTMC is a new, manually annotated, calibrated, multi-camera data set recorded outdoors on the Duke University campus with 8 synchronized cameras. It consists of:
-
-    8 static cameras x 85 minutes of 1080p 60 fps video
-    More than 2,000,000 manually annotated frames
-    More than 2,000 identities
-    Manual annotation by 5 people over 1 year
-    More identities than all existing MTMC datasets combined
-    Unconstrained paths, diverse appearance
-
-News
-
-    05 Feb 2019 We are organizing the 2nd Workshop on MTMCT and ReID at CVPR 2019
-    25 Jul 2018: The code for DeepCC is available on github
-    28 Feb 2018: OpenPose detections now available for download
-    19 Feb 2018: Our DeepCC tracker has been accepted to CVPR 2018
-    04 Oct 2017: A new blog post describes ID measures of performance
-    26 Jul 2017: Slides from the BMTT 2017 workshop are now available
-    09 Dec 2016: DukeMTMC is now hosted on MOTChallenge 
-
-DukeMTMC Downloads
-
-    DukeMTMC dataset (tracking)
-
-Dataset Extensions
-
-Below is a list of dataset extensions provided by the community:
-
-    DukeMTMC-VideoReID (download)
-    DukeMTMC-reID (download)
-    DukeMTMC4REID
-    DukeMTMC-attribute
-
-If you use or extend DukeMTMC, please refer to the license terms.
-DukeMTMCT Benchmark
-
-DukeMTMCT is a tracking benchmark hosted on motchallenge.net. Click here for the up-to-date rankings. Here you will find the official motchallenge-devkit used for evaluation by MOTChallenge. For detailed instructions how to submit on motchallenge you can refer to this link.
+![caption: Duke MTMC pixel-averaged image of camera #5 is shown with the bounding boxes for each student drawn in white. (c) Adam Harvey](assets/duke_mtmc_cam5_average_comp.jpg)
 
-Trackers are ranked using our identity-based measures which compute how often the system is correct about who is where, regardless of how often a target is lost and reacquired. Our measures are useful in applications such as security, surveillance or sports. This short post describes our measures with illustrations, while for details you can refer to the original paper.
-Tracking Systems
+### TODO
 
-We provide code for the following tracking systems which are all based on Correlation Clustering optimization:
+- change to heatmap overlay of each location
+- make fancy viz of foot trails with bbox and blurred persons
+- expand story
+- add google street view images of each camera location?
+- add actual head detections to header image with faces blurred
+- add 4 diverse example images with faces blurred
+- add map location of the brainwash cafe
 
-    DeepCC for single- and multi-camera tracking [1]
-    Single-Camera Tracker (demo video) [2]
-    Multi-Camera Tracker (demo video, failure cases) [2]
-    People-Groups Tracker [3]
-    Original Single-Camera Tracker [4]
+### Footnotes
\ No newline at end of file