Real-time motion data annotation via action string

Authors: Qi, T., Xiao, J., Zhuang, Y., Zhang, H., Yang, X., Zhang, J.J. and Feng, Y.

Journal: Computer Animation and Virtual Worlds

Volume: 25

Pages: 293-302

ISSN: 1546-4261


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.

Source: BURO EPrints