Abstract
Stochastic grammar has been used in many video analysis and event recognition applications as an efficient model to represent large-scale video activity. However, in previous works, due to the limitation on representing parallel temporal relations, traditional stochastic grammar cannot be used to model complex multi-agent activity including parallel temporal relations between sub-activities (such as “during” relation). In this paper, we extend the traditional grammar by introducing Temporal Relation Events (TRE) to solve the problem. The corresponding grammar parser appending complex temporal inference is also proposed. A system that can recognize two hands’ cooperative action in a “telephone calling” activity is built to demonstrate the effectiveness of our methods. In the experiment, a simple method to model the explicit state duration probability distribution in HMM detector is also proposed for accurate primitive events detection.
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Zhang, Z., Huang, K., Tan, T. (2006). Complex Activity Representation and Recognition by Extended Stochastic Grammar. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_16
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DOI: https://doi.org/10.1007/11612032_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
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