Abstract
In this paper, we propose an approach for human action recognition from different views in a knowledge transfer framework. Each frame in an action is considered as a sentence in an article. We believe that, though the appearance for the same action is quite different in different views, there exists some translation relationship between them. To abstract the relationship, we use the IBM Model 1 in statistical machine translation and the translation probabilities for vocabularies in the source view to those in the target view can be obtained from the training data. Consequently, we can translate an action based on the maximum a posteriori criterion. We validated our method on the public multi-view IXMAS dataset and obtained promising results compared to the state-of-the-art knowledge transfer based methods.
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Wang, J., Zheng, H. (2012). Cross-View Action Recognition Based on Statistical Machine Translation. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_32
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DOI: https://doi.org/10.1007/978-3-642-35136-5_32
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