Abstract
The precision of human interaction recognition mainly relies on the discrimination of action feature descriptors. The descriptors contained global and local information usually can be applied to classify interaction actions. A novel approach is proposed by using the Gist feature model to recognize human interaction actions, which has an advantage of simple feature, easy to operate, good real-time and flexible applications. Taking advantage of the theories with Gaussian pyramid and center-surround mechanism, the gist features from three channels are extracted to represent the human interaction motion, then the classification result is obtained by using frame to frame nearest neighbor classifier and weighted fusion them. The method is tested on UT-Interaction dataset. The experiments show that the method obtained stable performance with simple implementation.
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Ji, X., Zuo, X., Wang, C., Wang, Y. (2015). A Simple Human Interaction Recognition Based on Global GIST Feature Model. In: Liu, H., Kubota, N., Zhu, X., Dillmann, R., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2015. Lecture Notes in Computer Science(), vol 9244. Springer, Cham. https://doi.org/10.1007/978-3-319-22879-2_45
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DOI: https://doi.org/10.1007/978-3-319-22879-2_45
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