Abstract
We present a general data-driven method for multi-view action recognition relying on the appearance of dynamic systems captured from different viewpoints. Thus, we do not depend on 3d reconstruction, foreground segmentation, or accurate detections. We extend further earlier approaches based on Temporal Self-Similarity Maps by new low-level image features and similarity measures. Gaussian Process classification in combination with Histogram Intersection Kernels serve as powerful tools in our approach. Experiments performed on our new combined multi-view dataset as well as on the widely used IXMAS dataset show promising and competing results.
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Körner, M., Denzler, J. (2013). Temporal Self-Similarity for Appearance-Based Action Recognition in Multi-View Setups. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_19
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DOI: https://doi.org/10.1007/978-3-642-40261-6_19
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