Abstract
In this paper, a new framework is proposed for cross-view action recognition. Spatio-temporal patches are extracted as low-level feature and each patch is represented as a linear dynamical system (LDS). Bag of dynamical systems (BODS) is employed for middle-level representation. In order to bridge different views, we transform BODS pairs into a bilingual BODS through transferable dictionary pairs. Bilingual dictionaries are learned for the source and target view, which guarantee that the same action from the two views have same high-level representation. Support vector machine (SVM) is employed as the classifier. The experimental results on the IXMAS multi-view dataset show the effectiveness of proposed algorithm compared with others. The performance on the top view is also excellent.
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Chen, C., Yang, S., Gan, Z. (2013). Cross-View Action Recognition via Bilingual Bag of Dynamical Systems. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_25
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DOI: https://doi.org/10.1007/978-3-642-42057-3_25
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