Human body behavior recognition method based on Grassmann manifold analysis

The invention discloses a human body behavior recognition method based on Grassmann manifold analysis. The method comprises the steps: obtaining all training samples of a data set and mapping the training samples to a Grassmann manifold space; modeling the intra-class sample point distance and the i...

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Main Authors LI XIANGLI, XU BO, LI JIAOFEN, LUO JINGFENG, MENG RUXING, XU ZENGMIN, LI CHUNHAI, DING YONG
Format Patent
LanguageChinese
English
Published 07.08.2020
Subjects
CALCULATING
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Summary:The invention discloses a human body behavior recognition method based on Grassmann manifold analysis. The method comprises the steps: obtaining all training samples of a data set and mapping the training samples to a Grassmann manifold space; modeling the intra-class sample point distance and the inter-class sample point distance; redefining training samples on the data set; establishing a combined learning model; and carrying out iterative solution on the combined learning model; modeling and designing a classifier model by using various characteristics; generating a virtual label of the unlabeled video through a label propagation method from the labeled and unlabeled behavior videos based on the custom graph model, and revealing the correlation of the feature data by using multi-manifold analysis. For each type of features, the local structure consistency of neighbor data points is independently reserved, the global consistency of multiple feature data points is used in a training set to predict label data
Bibliography:Application Number: CN202010293342