@inproceedings{31ecd16adbd84fc9bdb7577bc260bfcc,
title = "MoDeep: A deep learning framework using motion features for human pose estimation",
abstract = "In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion (This dataset can be downloaded from http://cs.nyu.edu/∼ajain/accv2014/.), that extends the FLIC dataset [1] with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.",
author = "Arjun Jain and Jonathan Tompson and Yann LeCun and Christoph Bregler",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 12th Asian Conference on Computer Vision, ACCV 2014 ; Conference date: 01-11-2014 Through 05-11-2014",
year = "2015",
doi = "10.1007/978-3-319-16808-1_21",
language = "English (US)",
isbn = "9783319168074",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "302--315",
editor = "Ming-Hsuan Yang and Hideo Saito and Daniel Cremers and Ian Reid",
booktitle = "Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers",
}