Abstract：Due to the large sample and multiple characteristics of video sequence in the field of human action recognition, a method of kernel Fisher nonlinear discriminant (KFLD) - scale invariant feature transform (SIFT) and relevance vector machine (RVM) fuzzy integral fusion recognition based on tensor is proposed. Firstly, video sequence is pre-processed into binary video sequence, and then it is described as third-order tensor. Furthermore, as for large sample characteristics, a local feature extraction method of KFLD-SIFT is proposed to reduce the dimension around the key points under different initial scales. Meanwhile, RVM fuzzy integral fusion algorithm for behavior classification is presented. Finally, the proposed method and other relevant methods are compared through four kinds of evolution indexes and average recognition rates. The video sequence of KTH human action database and triple-cross verification method are used to test the recognition methods. Experimental results show that the proposed method achieves good recognition effect, and its average recognition rate rises by at least 2.3% compared to other mainstream methods for human action recognition.
肖迪，南雷光. 基于张量的KFLD-SIFT与RVM模糊积分融合的人体行为识别方法*[J]. 模式识别与人工智能, 2014, 27(8): 713-719.
XIAO Di, NAN Lei-Guang. KFLD-SIFT with RVM Fuzzy Integral Fusion Recognition of Human Action Based on Tensor. , 2014, 27(8): 713-719.
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2014, Vol. 27 
