Abstract
We present a novel method for human motion recognition. A video sequence is represented with a sparse set of spatial and spatial-temporal features by extracting static and dynamic interest points. Our model learns a set of poses along with the dynamics of the sequence. Pose models and the model of motion dynamics are represented as a constellation of static and dynamic parts, respectively. On top of the layer of individual models we build a higher level model that can be described as “constellation of constellation models”. This model encodes the spatial-temporal relationships between the dynamics of the motion and the appearance of individual poses. We test the model on a publicly available action dataset and demonstrate that our new method performs well on the classification tasks. We also perform additional experiments to show how the classification performance can be improved by increasing the number of pose models in our framework.
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Filipovych, R., Ribeiro, E. (2007). Combining Models of Pose and Dynamics for Human Motion Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2007. Lecture Notes in Computer Science, vol 4842. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76856-2_3
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DOI: https://doi.org/10.1007/978-3-540-76856-2_3
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