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凌志刚, 梁 彦, 潘 泉, 程咏梅, 赵春晖(西北工业大学自动化学院,西安 710072)

摘 要
Human Action Recognition Based on Tensor Subspace Learning

LING Zhigang, LIANG Yan, PAN Quan, CHENG Yongmei, ZHAO Chunhui(College of Automation, Northwestern Polytechnical University, Xi’an 710072)

In this paper, a simple but efficient algorithm based on tensor subspace learning is proposed to reduce the dimensionality of high-dimensional silhouette data for human action recognition. For image sequences of each action, they are projected into a low dimensional subspace so that both spatial and temporal properties of the action are preserved. Further, a nearest-neighbor action recognition is carried out basing on Hausdorff distance. Two experiments for action recognition and robust test have been carried out to testify the effectiveness of introduced tensor subspace learning.