Abstract
This paper presents a novel multi-view human action recognition approach based on a bag-of-key-poses. In the case of multi-view scenarios, it is especially difficult to perform accurate action recognition that still runs at an admissible recognition speed. The presented method aims to fill this gap by combining a silhouette-based pose representation with a simple, yet effective multi-view learning approach based on Model Fusion. Action classification is performed through efficient sequence matching and by the comparison of successive key poses which are evaluated on both feature similarity and match relevance. Experimentation on the MuHAVi dataset shows that the method outperforms currently available recognition rates and is exceptionally robust to actor-variance. Temporal evaluation confirms the method’s suitability for real-time recognition.
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Chaaraoui, A.A., Climent-Pérez, P., Flórez-Revuelta, F. (2012). An Efficient Approach for Multi-view Human Action Recognition Based on Bag-of-Key-Poses. In: Salah, A.A., Ruiz-del-Solar, J., Meriçli, Ç., Oudeyer, PY. (eds) Human Behavior Understanding. HBU 2012. Lecture Notes in Computer Science, vol 7559. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34014-7_3
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DOI: https://doi.org/10.1007/978-3-642-34014-7_3
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