Abstract
Pointing at objects is a natural form of interaction between humans that is of particular importance in human-machine interfaces. Our goal is the recognition of such deictic gestures on our mobile robot in order to enable a natural way of interaction. The approach proposed analyzes image data from the robot’s camera to detect the gesturing hand. We perform deictic gesture recognition through extending a trajectory recognition algorithm based on particle filtering with symbolic information from the objects in the vicinity of the acting hand. This vicinity is specified by a context area. By propagating the samples depending on a successful matching between expected and observed objects the samples that lack a corresponding context object are propagated less often. The results obtained demonstrate the robustness of the proposed system integrating trajectory data with symbolic information for deictic gesture recognition.
The work described in this paper was partially conducted within the EU Integrated Project COGNIRON (”The Cognitive Companion”) funded by the European Commission Division FP6-IST Future and Emerging Technologies under Contract FP6-002020 and supported by the German Research Foundation within the Graduate Program ’Task Oriented Communication’.
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Hofemann, N., Fritsch, J., Sagerer, G. (2004). Recognition of Deictic Gestures with Context. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_41
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DOI: https://doi.org/10.1007/978-3-540-28649-3_41
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