Abstract
We propose a method for human pose estimation based on Deep Neural Networks (DNNs). The pose estimation is formulated as a DNN-based regression problem towards body joints. We present a cascade of such DNN regressors which results in high precision pose estimates. The approach has the advantage of reasoning about pose in a holistic fashion and has a simple but yet powerful formulation which capitalizes on recent advances in Deep Learning. We present a detailed empirical analysis with state-of-art or better performance on four academic benchmarks of diverse real-world images.
Keywords
Computer scienceArtificial intelligencePoseArtificial neural networkEstimationDeep neural networksComputer visionPattern recognition (psychology)Machine learningEngineering
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Publication Info
- Year
- 2014
- Type
- article
- Pages
- 1653-1660
- Citations
- 3150
- Access
- Closed
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Cite This
Alexander Toshev,
Christian Szegedy
(2014).
DeepPose: Human Pose Estimation via Deep Neural Networks.
, 1653-1660.
https://doi.org/10.1109/cvpr.2014.214
Identifiers
- DOI
- 10.1109/cvpr.2014.214