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David van der Pol

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Publications by David van der Pol (bibliography)

2011

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Pol, David van der, Cuijpers, Raymond H. and Juola, James F. (2011): Head pose estimation for a domestic robot. In: Proceedings of the 6th International Conference on Human Robot Interaction 2011. pp. 277-278.

“Gaze direction is an important communicative cue. In order to use this cue for human-robot interaction, software needs to be developed that enables the estimation of head pose. We began by designing an application that is able to make a good estimate of the head pose, and, contrary to earlier head pose estimation approaches, that works for non-optimal lighting conditions. Initial results show that our approach using multiple networks trained with differing datasets, gives a good estimate of head pose, and it works well in poor lighting conditions and with low-resolution images. We validated our head pose estimation method using a custom built database of images of human heads. The actual head poses were measured using a trakStar (Ascension Technologies) six-degrees-of-freedom sensor. The head pose estimation algorithm allows us to assess a person's focus of attention, which allows robots to react in a timely fashion to dynamic human communicative cues.”

2010

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Pol, David van der, Cuijpers, Raymond H. and Juola, James F. (2010): Head pose estimation for real-time low-resolution video. In: Proceedings of the 2010 Annual European Conference on Cognitive Ergonomics 2010. pp. 353-354.

“Gaze direction is an important communicative cue. In order to use this cue for human-robot interaction, software needs to be developed that enables the estimation of head pose. We began by designing an application that is be able to make a good estimate of the head pose, and, contrary to earlier neural network approaches, that works for non-optimal lighting conditions. Initial results show that the approach using multiple networks trained with differing datasets, gives a good estimate of head pose, and it works well in poor lighting conditions. The solution is not optimal yet. Smart selection rules taking into account different lighting conditions would enable us to select the neural networks trained with images with similar lighting conditions. This research will allow us to use head orientation cues in Human-Robot interaction with low-resolution cameras and in poor lighting conditions. The software allows the robot to give a timely reaction to the dynamical communicative cues used by humans.”

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