diff options
| -rw-r--r-- | site/content/pages/datasets/brainwash/index.md | 38 |
1 files changed, 10 insertions, 28 deletions
diff --git a/site/content/pages/datasets/brainwash/index.md b/site/content/pages/datasets/brainwash/index.md index d9bffb39..6d90e78f 100644 --- a/site/content/pages/datasets/brainwash/index.md +++ b/site/content/pages/datasets/brainwash/index.md @@ -2,8 +2,8 @@ status: published title: Brainwash -desc: Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco -subdesc: The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection algorithms +desc: Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014 +subdesc: The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms slug: brainwash cssclass: dataset image: assets/background.jpg @@ -21,19 +21,18 @@ authors: Adam Harvey + Faces: 91,146 + Created by: Stanford University (US)<br>Max Planck Institute for Informatics (DE) + Funded by: Max Planck Center for Visual Computing and Communication -+ Purpose: Face detection ++ Purpose: Head detection ++ Download Size: 4.1GB + Website: <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">stanford.edu</a> ## Brainwash Dataset -(PAGE UNDER DEVELOPMENT) +*Brainwash* is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe"[^readme] captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com [cite orig paper]. -*Brainwash* is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe[^readme]". The images are used to train and validate algorithms for detecting people in crowded scenes. +Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance [^localized_region_context] [^replacement_algorithm]. -Before closing in 2017, The Brainwash Cafe was a combination cafe, laundromat, and performance venue located in San Francisco's SoMA district. The images used for Brainwash dataset were captured on 3 days: October 27, November 13, and November 24 in 2014. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com [cite orig paper]. - -Brainwash is not a widely used dataset but since it's publication by Stanford in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on "people head detection in crowded scenes" [^localized_region_context] [^replacement_algorithm]. +If you happen to have been at Brainwash cafe in San Franscisco at any time on October 26, November 13, or November 24 in 2014 you are most likely included in the Brainwash dataset.  @@ -44,42 +43,25 @@ Brainwash is not a widely used dataset but since it's publication by Stanford in {% include 'map.html' %} -Add more analysis here - +{% include 'citations.html' %} {% include 'supplementary_header.html' %} -{% include 'citations.html' %} -   -### Additional Information +#### Additional Resources - The dataset author spoke about his research at the CVPR conference in 2016 <https://www.youtube.com/watch?v=Nl2fBKxwusQ> -To evaluate the performance of our approach, we collected a -large dataset of images from busy scenes using video footage available from public webcams. In -total, we collect 11917 images with 91146 labeled people. We extract images from video footage at -a fixed interval of 100 seconds to ensure a large variation in images. We allocate 1000 images for -testing and validation, and leave the remaining images for training, making sure that no temporal -overlaps exist between training and test splits. The resulting training set contains 82906 instances. -Test and validation sets contain 4922 and 3318 people instances respectively. Images were labeled -using Amazon Mechanical Turk by a handful of workers pre-selected through their performance on -an example task. We label each person’s head to avoid ambiguity in bounding box locations. The -annotator labels any person she is able to recognize, even if a substantial part of the person is not -visible. Images and annotations will be made available 1 . -Examples of collected images are shown in Fig. 6, and in the video included in the supplemental -material. Images in our dataset include challenges such as people at small scales, strong partial -occlusions, and a large variability in clothing and appearance. - TODO - add bounding boxes to the header image - remake montage with randomized images, with bboxes - clean up intro text +- verify quote citations ### Footnotes |