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Accurate identification of people’s pose in video is of great importance. Its applications include gesture-based controls such as Kinect and motion capture systems without markers. New York University researchers recently developed a deep learning architecture using both color and motion features for human pose estimation.
The deep learning framework, named MoDeep, based on a multi-resolution convolutional network, was published in a recent paper "MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation". The study has also proposed new motion features and created a new dataset called FLIC-motion by augmenting the Frames Labeled In Cinema (FLIC) dataset with the proposed motion features. According to the paper, MoDeep has been tested on the FLIC-motion dataset and outperforms existing state-of-the-art techniques for the task of human body pose detection in video.
For more details about MoDeep, please visit http://cs.nyu.edu/~ajain/accv2014/.
|Nominations Open for 2020 SPS Awards||1 September 2020|
|Call for Nominations: Awards Board and Nominations and Appointments Committee||25 September 2020|
|Call for Nominations: Fellow Evaluation Committee||30 September 2020|
|Election of Regional Directors-at-Large and Members-at-Large||1 October 2020|
|Meet the 2020 Candidates: IEEE President-Elect and Division IX Director-Elect||1 October 2020|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2020|
|Call for Nominations Extended: Chair, Young Professionals Committee||16 October 2020|
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