Multi-angle indoor human action recognition method based on 3D skeleton

Abstract

The invention discloses a multi-angle indoor human action recognition method based on a 3D skeleton. The multi-angle indoor human action recognition method comprises the following steps that 1) videos of human motions at three angles of a front angle, a squint angle and a side angle are acquired; and the videos include training videos and test videos; 2) human skeleton 3D features in the videos are extracted through body feeling equipment; and the three-dimensional skeleton features include global motion features and arm and leg local motion features; 3) model training is performed; feature description is performed through the human skeleton 3D features in the training videos so that a training feature set is obtained; and the concrete process is listed as follows: online dictionary learning of the three-dimensional skeleton features is performed; and then dimension reduction is performed through sparse principal component analysis so that a feature set data set is formed; and 4) the feature set of the samples of the test videos is inputted, and recognition is performed through a linear support vector machine (LSVM). Classified recognition of the multi-angle motions is realized by the method so that the limitation of a single-angle recognition algorithm can be overcome and thus the method has more research value and actual application value.

Description

technical field [0001] The invention relates to the field of human behavior recognition, in particular to a multi-view indoor human behavior recognition method based on a 3D skeleton. Background technique [0002] In recent years, action recognition has become a research hotspot in the fields of computer vision and pattern recognition. Behavior recognition technology is not only used in many fields such as video surveillance systems, smart homes, sports operation analysis, human-computer interaction, film and television action production, and medical rehabilitation, but also creates huge social and economic benefits. According to different behavior description methods, the existing algorithms can be divided into two categories: one is the method based on the appearance, and the other is the method based on the human body model. and so on to describe the behavior; while the method based on the human body model uses the human body model to obtain the structural characteristic...

AI Technical Summary

Problems solved by technology

Shotton et al. proposed an algorithm that uses depth images to obtain 3D skeleton positions, but only simple joint points represent the skeleton, and do not analyze information such as skeleton joint point rotation.

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