关键词: 多视角非负矩阵分解, 时空矩阵, 共识矩阵, 最大相关系数

Abstract: This paper proposes an view-invariant feature extraction method based on multi-view non-negative matrix factorization to fuse multi-view information and recognize human activity. The spatio-temporal descriptors of each video frame are extracted to describe motion and morphological information in the video scene. In order to solve the effect of observation angle change on recognition, spatio-temporal matrix based on spatio-temporal descriptors is constructed from different perspectives, and multi-view non-negative matrix factorization is used to construct multi-view objective functions to obtain a consensus matrix with multi-view information. The behavior is classified by calculating the maximum correlation coefficient of the consensus matrix. This method is validated on WVU dataset and i3Dpose dataset, and compared with other methods. The results show that the method is effective in behavior recognition.

Key words: multi-view non-negative matrix factorization, spatio-temporal matrix, consensus matrix, maximum correlation coefficient