Scipediacontent: Scipediacontent moved page Draft Content 238439824 to Rieger et al 2019a

2021-01-21T13:43:02Z

Scipediacontent moved page Draft Content 238439824 to Rieger et al 2019a

Scipediacontent: Created page with " == Abstract == Head poses are a key component of human bodily communication and thus a decisive element of human-computer interaction. Real-time head pose estimation is cruc..."

2021-01-21T13:42:59Z

Created page with " == Abstract == Head poses are a key component of human bodily communication and thus a decisive element of human-computer interaction. Real-time head pose estimation is cruc..."

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== Abstract ==

Head poses are a key component of human bodily communication and thus a decisive element of human-computer interaction. Real-time head pose estimation is crucial in the context of human-robot interaction or driver assistance systems. The most promising approaches for head pose estimation are based on Convolutional Neural Networks (CNNs). However, CNN models are often too complex to achieve real-time performance. To face this challenge, we explore a popular subgroup of CNNs, the Residual Networks (ResNets) and modify them in order to reduce their number of parameters. The ResNets are modifed for different image sizes including low-resolution images and combined with a varying number of layers. They are trained on in-the-wild datasets to ensure real-world applicability. As a result, we demonstrate that the performance of the ResNets can be maintained while reducing the number of parameters. The modified ResNets achieve state-of-the-art accuracy and provide fast inference for real-time applicability.

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1906.05203 http://arxiv.org/abs/1906.05203]

* [http://arxiv.org/pdf/1906.05203 http://arxiv.org/pdf/1906.05203]

* [http://link.springer.com/content/pdf/10.1007/978-3-030-22999-3_12 http://link.springer.com/content/pdf/10.1007/978-3-030-22999-3_12],
: [http://dx.doi.org/10.1007/978-3-030-22999-3_12 http://dx.doi.org/10.1007/978-3-030-22999-3_12] under the license http://www.springer.com/tdm

* [https://dblp.uni-trier.de/db/journals/corr/corr1906.html#abs-1906-05203 https://dblp.uni-trier.de/db/journals/corr/corr1906.html#abs-1906-05203],
: [https://arxiv.org/pdf/1906.05203 https://arxiv.org/pdf/1906.05203],
: [https://arxiv.org/abs/1906.05203 https://arxiv.org/abs/1906.05203],
: [https://ui.adsabs.harvard.edu/abs/2019arXiv190605203R/abstract https://ui.adsabs.harvard.edu/abs/2019arXiv190605203R/abstract],
: [http://export.arxiv.org/pdf/1906.05203 http://export.arxiv.org/pdf/1906.05203],
: [http://export.arxiv.org/abs/1906.05203 http://export.arxiv.org/abs/1906.05203],
: [https://academic.microsoft.com/#/detail/2953397651 https://academic.microsoft.com/#/detail/2953397651]

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Rieger et al 2019a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 238439824 to Rieger et al 2019a

Scipediacontent: Created page with " == Abstract == Head poses are a key component of human bodily communication and thus a decisive element of human-computer interaction. Real-time head pose estimation is cruc..."