MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
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.ny...
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| Published in | Computer Vision -- ACCV 2014 Vol. 9004; pp. 302 - 315 |
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| Main Authors | , , , |
| Format | Book Chapter |
| Language | English |
| Published |
Switzerland
Springer International Publishing AG
2015
Springer International Publishing |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783319168074 331916807X |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-319-16808-1_21 |
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| 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. |
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| AbstractList | 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 | Tompson, Jonathan Jain, Arjun Bregler, Christoph LeCun, Yann |
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| Copyright | Springer International Publishing Switzerland 2015 |
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| DOI | 10.1007/978-3-319-16808-1_21 |
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| Editor | Saito, Hideo Yang, Ming-Hsuan Cremers, Daniel Reid, Ian |
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| PublicationSubtitle | 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part II |
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| Snippet | In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which... |
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| SubjectTerms | Artificial intelligence Convolutional Layer Convolutional Network Image processing Laplacian Pyramid Motion Feature Optical Flow Pattern recognition |
| Title | MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation |
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