Qi, T., Xiao, J., Zhuang, Y., Zhang, H., Yang, X., Zhang, J. J. and Feng, Y., 2014. Real-time motion data annotation via action string. Computer Animation and Virtual Worlds, 25, 293 - 302 .
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Real-time_motion_data_annotation_via_action_string.pdf - Published Version
Official URL: http://dx.doi.org/10.1002/cav.1590
Even though there is an explosive growth of motion capture data, there is still a lack of efficient and reliable methods to automatically annotate all the motions in a database. Moreover, because of the popularity of mocap devices in home entertainment systems, real-time human motion annotation or recognition becomes more and more imperative. This paper presents a new motion annotation method that achieves both the aforementioned two targets at the same time. It uses a probabilistic pose feature based on the Gaussian Mixture Model to represent each pose. After training a clustered pose feature model, a motion clip could be represented as an action string. Then, a dynamic programming-based string matching method is introduced to compare the differences between action strings. Finally, in order to achieve the real-time target, we construct a hierarchical action string structure to quickly label each given action string. The experimental results demonstrate the efficacy and efficiency of our method.
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||01 Sep 2014 11:12|
|Last Modified:||01 Sep 2014 11:12|
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