Abstract
In this contribution we introduce a novel method for 3D trajectory based recognition and discrimination between different working actions and long-term motion prediction. The 3D pose of the human hand-forearm limb is tracked over time with a multi-hypothesis Kalman Filter framework using the Multiocular Contracting Curve Density algorithm (MOCCD) as a 3D pose estimation method. A novel trajectory classification approach is introduced which relies on the Levenshtein Distance on Trajectories (LDT) as a measure for the similarity between trajectories. Experimental investigations are performed on 10 real-world test sequences acquired from different viewpoints in a working environment. The system performs the simultaneous recognition of a working action and a cognitive long-term motion prediction. Trajectory recognition rates around 90% are achieved, requiring only a small number of training sequences. The proposed prediction approach yields significantly more reliable results than a Kalman Filter based reference approach.
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Hahn, M., Krüger, L., Wöhler, C. (2008). 3D Action Recognition and Long-Term Prediction of Human Motion. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_3
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DOI: https://doi.org/10.1007/978-3-540-79547-6_3
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