Human Pose Estimation by a Series of Residual Auto-Encoders
In: Pattern Recognition and Image Analysis ISBN: 9783319588377 IbPRIA; (2017)
Online
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Zugriff:
Pose estimation is the task of predicting the pose of an object in an image or in a sequence of images. Here, we focus on articulated human pose estimation in scenes with a single person. We employ a series of residual auto-encoders to produce multiple predictions which are then combined to provide a heatmap prediction of body joints. In this network topology, features are processed across all scales which captures the various spatial relationships associated with the body. Repeated bottom-up and top-down processing with intermediate supervision for each auto-encoder network is applied. We propose some improvements to this type of regression-based networks to further increase performance, namely: (a) increase the number of parameters of the auto-encoder networks in the pipeline, (b) use stronger regularization along with heavy data augmentation, (c) use sub-pixel precision for more precise joint localization, and (d) combine all auto-encoders output heatmaps into a single prediction, which further increases body joint prediction accuracy. We demonstrate state-of-the-art results on the popular FLIC and LSP datasets. FCT project LARSyS [UID/EEA/50009/2013] FCT PhD grant [SFRH/BD/79812/2011]
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Human Pose Estimation by a Series of Residual Auto-Encoders
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Autor/in / Beteiligte Person: | Farrajota, Miguel ; J. M. H. du Buf ; Rodrigues, João M. F. ; Alexandre, L. A. ; Sanchez, J. S. ; Rodrigues, J. M. F. |
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Quelle: | Pattern Recognition and Image Analysis ISBN: 9783319588377 IbPRIA; (2017) |
Veröffentlichung: | Springer International Publishing, 2017 |
Medientyp: | unknown |
ISBN: | 978-3-319-58837-7 (print) |
DOI: | 10.1007/978-3-319-58838-4_15 |
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