Human body movement recognition method based on surveillance isometric mapping

Abstract

The invention discloses a human body movement recognition method based on surveillance isometric mapping, and belongs to the field of pattern recognition and computer vision. The human body movement recognition method comprises the following steps; S1, performing foreground extraction through codebook method for the video to acquire a binarized human body foreground image; S2, performing morphology processing and normalization for the human body foreground image to acquire a human body silhouette image; S3, performing periodization analysis for the human body silhouette image sequence, wherein each video is represented by a series of key frames comprising a complete movement period; S4, performing vectorization for the key frames of the human body silhouette image, and performing characteristic dimension reduction through surveillance isometric mapping; S5, recognizing the characteristic after dimension reduction through the nearest categorizer according to Hausdorff distance. The human body movement recognition method breaks through the limitation of the conventional algorithm, and reduces complexity of the algorithm while increasing the categorizing accuracy, thereby being more feasible in practical engineering application.

Description

technical field [0001] The invention relates to the field of pattern recognition and computer vision, in particular to a human action recognition method based on supervised isometric mapping. Background technique [0002] The purpose of human motion analysis is to recognize and understand human actions, and research based on this is called action recognition. Today, with the rapid development of science and technology, motion recognition is used in many fields such as intelligent monitoring, identity confirmation, motion analysis, human-computer interaction, etc., and has relatively high academic theoretical value and extensive market practical value in these fields. [0003] In recent years, human action recognition has gradually become a very challenging research direction in computer vision, and great progress has been made, but there are still many problems to be solved, and the existing methods are far from meeting the needs of practical applications. There is still a ...

AI Technical Summary

Problems solved by technology

In some existing methods, in order to use as much information as possible to represent actions, a variety of feature fusion methods are used. However, this puts forward higher requirements for computing speed to a certain extent. Difficult to identify, counterproductive to confusion

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