Abstract
Poses and gestures are an important part of the nonverbal inter-human communication. In the last years many different methods for estimating poses and gestures in the field of Human-Machine-Interfaces were developed. In this paper for the first time we present an experimental comparison of several re-implemented Neural Network based approaches for a demanding visual instruction task on a mobile system. For the comparison we used several Neural Networks (Neural Gas, SOM, LLM, PSOM and MLP) and a k-Nearest-Neighbourhood classificator on a common data set of images, which we recorded on our mobile robot Horos under real world conditions. For feature extraction we use Gaborjets and the features of a special histogram on the image. We also compare the results of the different approaches with the results of human subjects who estimated the target point of a pointing pose. The results obtained demonstrate that a cascade of MLPs is best suited to cope with the task and achieves results equal to human subjects.
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Steege, FF., Martin, C., Groß, HM. (2007). Estimation of Pointing Poses on Monocular Images with Neural Techniques - An Experimental Comparison. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_61
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DOI: https://doi.org/10.1007/978-3-540-74695-9_61
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